Similarity Search and Applications: 13th International Conference, SISAP 2020, Copenhagen, Denmark, September 30 – October 2, 2020, Proceedings

s of Keynotes Interactive Exploration using Hypergraphs

[1]  I. J. Schoenberg.  Metric spaces and completely monotone functions , 1938 .

[2]  Richard A. Harshman,et al.  Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .

[3]  Claudio Gennaro,et al.  YFCC100M HybridNet fc6 Deep Features for Content-Based Image Retrieval , 2016, MMCommons @ ACM Multimedia.

[4]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[5]  Thomas Bartz-Beielstein,et al.  Experimental Methods for the Analysis of Optimization Algorithms , 2010 .

[6]  Yuanming Zhang,et al.  A Memory-Efficient Sketch Method for Estimating High Similarities in Streaming Sets , 2019, KDD.

[7]  Tara N. Sainath,et al.  State-of-the-Art Speech Recognition with Sequence-to-Sequence Models , 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  Miroslav Chlebík,et al.  Approximation Hardness of Dominating Set Problems , 2004, ESA.

[9]  Aleatha Parker-Wood,et al.  Utility Preserving Secure Private Data Release , 2019, ArXiv.

[10]  Nir Ailon,et al.  Fast Dimension Reduction Using Rademacher Series on Dual BCH Codes , 2008, SODA '08.

[11]  J. Leeuw,et al.  Principal component analysis of three-mode data by means of alternating least squares algorithms , 1980 .

[12]  Thomas Seidl,et al.  Signature Quadratic Form Distance , 2010, CIVR '10.

[13]  Eric C. Chi,et al.  Making Tensor Factorizations Robust to Non-Gaussian Noise , 2010, 1010.3043.

[14]  Andrea Esuli,et al.  Transformer Reasoning Network for Image- Text Matching and Retrieval , 2020, 2020 25th International Conference on Pattern Recognition (ICPR).

[15]  Angelo C. Loula,et al.  Recognition of Static Gestures Applied to Brazilian Sign Language (Libras) , 2015, 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images.

[16]  Arthur Zimek,et al.  ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg" , 2019, ArXiv.

[17]  Victor S. Lempitsky,et al.  Efficient Indexing of Billion-Scale Datasets of Deep Descriptors , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  James Bailey,et al.  Characteristics of Local Intrinsic Dimensionality (LID) in Subspaces: Local Neighbourhood Analysis , 2019, SISAP.

[19]  Marco Patella,et al.  Approximate similarity search: A multi-faceted problem , 2009, J. Discrete Algorithms.

[20]  Cordelia Schmid,et al.  Improving Bag-of-Features for Large Scale Image Search , 2010, International Journal of Computer Vision.

[21]  Richard C. H. Connor,et al.  Quantifying the Specificity of Near-duplicate Image Classification Functions , 2016, VISIGRAPP.

[22]  Heiko Schuldt,et al.  Multi-Stage Queries and Temporal Scoring in Vitrivr , 2020, 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

[23]  Susumu Serita,et al.  Probabilistic enhancement of approximate indexing in metric spaces , 2013, Inf. Syst..

[24]  Jakub Lokoc,et al.  Ptolemaic access methods: Challenging the reign of the metric space model , 2013, Inf. Syst..

[25]  Wolfgang Hackbusch,et al.  An Introduction to Hierarchical (H-) Rank and TT-Rank of Tensors with Examples , 2011, Comput. Methods Appl. Math..

[26]  James Bailey,et al.  Measuring dependency via intrinsic dimensionality , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[27]  Nora Reyes,et al.  Permutation's Signatures for Proximity Searching in Metric Spaces , 2019, SISAP.

[28]  Ken-ichi Kawarabayashi,et al.  Estimating Local Intrinsic Dimensionality , 2015, KDD.

[29]  Pavel Zezula,et al.  M-tree: An Efficient Access Method for Similarity Search in Metric Spaces , 1997, VLDB.

[30]  Sanjay Chawla,et al.  Density-preserving projections for large-scale local anomaly detection , 2012, Knowledge and Information Systems.

[31]  Nina Mishra,et al.  Privacy via the Johnson-Lindenstrauss Transform , 2012, J. Priv. Confidentiality.

[32]  Satoshi Nakamura,et al.  Compressing recurrent neural network with tensor train , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[33]  Sakti Pramanik,et al.  Efficient search scheme for very large image databases , 1999, Electronic Imaging.

[34]  Pavel Zezula,et al.  Similarity Search - The Metric Space Approach , 2005, Advances in Database Systems.

[35]  Claudio Gennaro,et al.  Large-scale instance-level image retrieval , 2020, Inf. Process. Manag..

[36]  Piotr Indyk,et al.  Approximate nearest neighbors: towards removing the curse of dimensionality , 1998, STOC '98.

[37]  Kim Batselier,et al.  Faster Tensor Train Decomposition for Sparse Data , 2019, J. Comput. Appl. Math..

[38]  Chang-Woo Lee.  DOMINATION IN DIGRAPHS , 1998 .

[39]  Hisashi Kashima,et al.  Fast Similarity Computation in Factorized Tensors , 2012, SISAP.

[40]  Benjamin Bustos,et al.  On nonmetric similarity search problems in complex domains , 2011, CSUR.

[41]  Claudio Gennaro,et al.  Re-ranking via local embeddings: A use case with permutation-based indexing and the nSimplex projection , 2021, Inf. Syst..

[42]  Heiko Schuldt,et al.  Deep Learning-Based Concept Detection in vitrivr , 2018, MMM.

[43]  Lei Chen,et al.  Finding time period-based most frequent path in big trajectory data , 2013, SIGMOD '13.

[44]  Ken-ichi Kawarabayashi,et al.  Intrinsic Dimensionality Estimation within Tight Localities , 2019, SDM.

[45]  Panos Kalnis,et al.  Trajectory Similarity Join in Spatial Networks , 2017, Proc. VLDB Endow..

[46]  Peer Kröger,et al.  Dimensional Testing for Reverse k-Nearest Neighbor Search , 2017, Proc. VLDB Endow..

[47]  S. Canu,et al.  Training Invariant Support Vector Machines using Selective Sampling , 2005 .

[48]  Michael Gertz,et al.  Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection - A Remedy Against the Curse of Dimensionality? , 2017, SISAP.

[49]  Rong Jin,et al.  Visual Search at Alibaba , 2018, KDD.

[50]  Qi Tian,et al.  Large scale image search with geometric coding , 2011, ACM Multimedia.

[51]  Fabrizio Falchi,et al.  Re-implementing and Extending Relation Network for R-CBIR , 2020, IRCDL.

[52]  Huan Liu,et al.  Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution , 2003, ICML.

[53]  Thomas Dybdahl Ahle.  On the Problem of $p_1^{-1}$ in Locality-Sensitive Hashing , 2020 .

[54]  Quoc V. Le,et al.  EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.

[55]  Ronan Sicre,et al.  Particular object retrieval with integral max-pooling of CNN activations , 2015, ICLR.

[56]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[57]  Christian S. Jensen,et al.  Indexing Metric Spaces for Exact Similarity Search , 2020, ArXiv.

[58]  Martin Ester,et al.  Density‐based clustering , 2019, WIREs Data Mining Knowl. Discov..

[59]  Maribel Yasmina Santos,et al.  Concave hull: A k-nearest neighbours approach for the computation of the region occupied by a set of points , 2007, GRAPP.

[60]  Rasmus Pagh,et al.  PUFFINN: Parameterless and Universally Fast FInding of Nearest Neighbors , 2019, ESA.

[61]  Andrea Esuli,et al.  CoPhIR: a Test Collection for Content-Based Image Retrieval , 2009, ArXiv.

[62]  Eleftherios Tiakas,et al.  Scalable Trajectory Similarity Search Based on Locations in Spatial Networks , 2015, MEDI.

[63]  Alexandr Andoni,et al.  Practical and Optimal LSH for Angular Distance , 2015, NIPS.

[64]  Kim Batselier.  The trouble with tensor ring decompositions , 2018, ArXiv.

[65]  Jen-Hao Hsiao,et al.  Deep learning of binary hash codes for fast image retrieval , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[66]  Piotr Indyk,et al.  Similarity Search in High Dimensions via Hashing , 1999, VLDB.

[67]  M. Gromov.  Groups of polynomial growth and expanding maps , 1981 .

[68]  Qiang Huang,et al.  Query-Aware Locality-Sensitive Hashing for Approximate Nearest Neighbor Search , 2015, Proc. VLDB Endow..

[69]  K. Selçuk Candan,et al.  LWI-SVD: low-rank, windowed, incremental singular value decompositions on time-evolving data sets , 2014, KDD.

[70]  Manoranjan Dash,et al.  Dimensionality reduction of unsupervised data , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[71]  Surajit Chaudhuri,et al.  A Primitive Operator for Similarity Joins in Data Cleaning , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[72]  Anna Monreale,et al.  WhereNext: a location predictor on trajectory pattern mining , 2009, KDD.

[73]  David Novak,et al.  PPP-Codes for Large-Scale Similarity Searching , 2016, Trans. Large Scale Data Knowl. Centered Syst..

[74]  Eric Nyberg,et al.  Pruning Algorithms for Low-Dimensional Non-metric k-NN Search: A Case Study , 2019, SISAP.

[75]  Larry Carter,et al.  Universal Classes of Hash Functions , 1979, J. Comput. Syst. Sci..

[76]  Richard C. H. Connor,et al.  Supermetric Search , 2017, Inf. Syst..

[77]  George Awad,et al.  On Influential Trends in Interactive Video Retrieval: Video Browser Showdown 2015–2017 , 2018, IEEE Transactions on Multimedia.

[78]  Panos Kalnis,et al.  Personalized trajectory matching in spatial networks , 2014, The VLDB Journal.

[79]  Jie Zhu,et al.  Effective and efficient trajectory outlier detection based on time-dependent popular route , 2016, World Wide Web.

[80]  Peter J. Bickel,et al.  Maximum Likelihood Estimation of Intrinsic Dimension , 2004, NIPS.

[81]  Zheng Yan,et al.  An Empirical Comparison of FAISS and FENSHSES for Nearest Neighbor Search in Hamming Space , 2019, eCOM@SIGIR.

[82]  Carlos Arango,et al.  Performance Evaluation of Container-based Virtualization for High Performance Computing Environments , 2017, Revista UIS Ingenierías.

[83]  Aaron Roth,et al.  The Algorithmic Foundations of Differential Privacy , 2014, Found. Trends Theor. Comput. Sci..

[84]  Ken-ichi Kawarabayashi,et al.  Extreme-value-theoretic estimation of local intrinsic dimensionality , 2018, Data Mining and Knowledge Discovery.

[85]  Catherine C. McGeoch.  Experimental Methods for Algorithm Analysis , 2008, Encyclopedia of Algorithms.

[86]  Tao Lei.  Scaling limit of random forests with prescribed degree sequences , 2018 .

[87]  Jonathan Drake,et al.  Accelerated k-means with adaptive distance bounds , 2012 .

[88]  Agma J. M. Traina,et al.  Efficient Indexing of Multiple Metric Spaces with Spectra , 2019, 2019 IEEE International Symposium on Multimedia (ISM).

[89]  Christos Faloutsos,et al.  The Omni-family of all-purpose access methods: a simple and effective way to make similarity search more efficient , 2007, The VLDB Journal.

[90]  R. Huisman,et al.  Tail-Index Estimates in Small Samples , 2001 .

[91]  Md Zahidul Islam,et al.  Missing value imputation using decision trees and decision forests by splitting and merging records: Two novel techniques , 2013, Knowl. Based Syst..

[92]  Hanan Samet,et al.  Index-driven similarity search in metric spaces (Survey Article) , 2003, TODS.

[93]  Luisa Micó,et al.  A new version of the nearest-neighbour approximating and eliminating search algorithm (AESA) with linear preprocessing time and memory requirements , 1994, Pattern Recognit. Lett..

[94]  Christopher D. Manning,et al.  Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..

[95]  Christian Sohler,et al.  BICO: BIRCH Meets Coresets for k-Means Clustering , 2013, ESA.

[96]  J. Chang,et al.  Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .

[97]  Geoffrey E. Hinton,et al.  Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.

[98]  Ping Li,et al.  b-Bit minwise hashing , 2009, WWW '10.

[99]  Rasmus Pagh,et al.  On the Complexity of Inner Product Similarity Join , 2015, PODS.

[100]  Michael E. Houle,et al.  Local Intrinsic Dimensionality I: An Extreme-Value-Theoretic Foundation for Similarity Applications , 2017, SISAP.

[101]  Hans-Peter Kriegel,et al.  Data bubbles: quality preserving performance boosting for hierarchical clustering , 2001, SIGMOD '01.

[102]  Luca Rossetto,et al.  Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019 , 2020, IEEE Transactions on Multimedia.

[103]  Maria Luisa Sapino,et al.  Data Management for Multimedia Retrieval , 2010 .

[104]  Michael E. Houle,et al.  Dimensional Testing for Multi-step Similarity Search , 2012, 2012 IEEE 12th International Conference on Data Mining.

[105]  Tomás Skopal,et al.  Unified framework for fast exact and approximate search in dissimilarity spaces , 2007, TODS.

[106]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[107]  Takafumi Kanamori,et al.  Density Ratio Estimation in Machine Learning , 2012 .

[108]  Rina Panigrahy,et al.  Entropy based nearest neighbor search in high dimensions , 2005, SODA '06.

[109]  David A. Patterson,et al.  In-datacenter performance analysis of a tensor processing unit , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).

[110]  Hans-Peter Kriegel,et al.  The (black) art of runtime evaluation: Are we comparing algorithms or implementations? , 2017, Knowledge and Information Systems.

[111]  Zheng Yan,et al.  Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines , 2019, SISAP.

[112]  L. Tucker,et al.  Some mathematical notes on three-mode factor analysis , 1966, Psychometrika.

[113]  David R. Karger,et al.  Finding nearest neighbors in growth-restricted metrics , 2002, STOC '02.

[114]  Takeshi Shinohara,et al.  Efficient Similarity Search by Reducing I/O with Compressed Sketches , 2009, 2009 Second International Workshop on Similarity Search and Applications.

[115]  Ralf Hartmut Güting,et al.  Indexing the Trajectories of Moving Objects in Networks* , 2004, GeoInformatica.

[116]  R. Abraham,et al.  A note on the Gromov-Hausdorff-Prokhorov distance between (locally) compact metric measure spaces , 2012, 1202.5464.

[117]  Wilfred Ng,et al.  Locality-sensitive hashing scheme based on dynamic collision counting , 2012, SIGMOD Conference.

[118]  J. Karamata,et al.  Sur un mode de croissance régulière. Théorèmes fondamentaux , 1933 .

[119]  Shie Mannor,et al.  Scalable Detection of Offensive and Non-compliant Content / Logo in Product Images , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[120]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[121]  Moses Charikar,et al.  Similarity estimation techniques from rounding algorithms , 2002, STOC '02.

[122]  Yannis Manolopoulos,et al.  Trajectory Similarity Search in Spatial Networks , 2006, 2006 10th International Database Engineering and Applications Symposium (IDEAS'06).

[123]  Sergey Brin,et al.  Near Neighbor Search in Large Metric Spaces , 1995, VLDB.

[124]  A. Winsor.  Sampling techniques. , 2000, Nursing times.

[125]  Reinhold Schneider,et al.  The Alternating Linear Scheme for Tensor Optimization in the Tensor Train Format , 2012, SIAM J. Sci. Comput..

[126]  Raymond E. Bonner,et al.  On Some Clustering Techniques , 1964, IBM J. Res. Dev..

[127]  Ping Li,et al.  Theory and applications of b-bit minwise hashing , 2011, Commun. ACM.

[128]  Anastasios Gounaris,et al.  Exact Set Similarity Joins for Large Datasets in the GPGPU paradigm , 2019, DaMoN.

[129]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[130]  Angsuman Das.  Partial Domination in Graphs , 2017, Iranian Journal of Science and Technology, Transactions A: Science.

[131]  Parth Nagarkar,et al.  PSLSH: An Index Structure for Efficient Execution of Set Queries in High-Dimensional Spaces , 2018, CIKM.

[132]  James Bailey,et al.  Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality , 2018, ICLR.

[133]  Ngai Wong,et al.  Computing low-rank approximations of large-scale matrices with the Tensor Network randomized SVD , 2018, SIAM J. Matrix Anal. Appl..

[134]  Agma J. M. Traina,et al.  Parameter-free and domain-independent similarity search with diversity , 2013, SSDBM.

[135]  Bin Li,et al.  POISketch: Semantic Place Labeling over User Activity Streams , 2016, IJCAI.

[136]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[137]  David Pisinger,et al.  Upper bounds and exact algorithms for p-dispersion problems , 2006, Comput. Oper. Res..

[138]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[139]  Dong Xu,et al.  Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey , 2018, IEEE Signal Processing Magazine.

[140]  Michael E. Houle,et al.  Rank-Based Similarity Search: Reducing the Dimensional Dependence , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[141]  George Awad,et al.  V3C - a Research Video Collection , 2018, MMM.

[142]  Panos Kalnis,et al.  User oriented trajectory search for trip recommendation , 2012, EDBT '12.

[143]  Volker Tresp,et al.  Tensor-Train Recurrent Neural Networks for Video Classification , 2017, ICML.

[144]  M. H. Eggar,et al.  A tournament problem , 2003, Discret. Math..

[145]  Cynthia Dwork,et al.  Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.

[146]  Evaggelia Pitoura,et al.  Diversity in Big Data: A Review , 2017, Big Data.

[147]  Liqing Zhang,et al.  Tensor Ring Decomposition , 2016, ArXiv.

[148]  Ke Wang,et al.  Continuous similarity search for evolving queries , 2015, Knowledge and Information Systems.

[149]  Philip J. Guo.  CDE: A Tool for Creating Portable Experimental Software Packages , 2012, Computing in Science & Engineering.

[150]  Richard C. H. Connor,et al.  High-Dimensional Simplexes for Supermetric Search , 2017, SISAP.

[151]  Cristina Ioana Muntean,et al.  High-Quality Prediction of Tourist Movements using Temporal Trajectories in Graphs , 2020, 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[152]  Andrei Z. Broder,et al.  On the resemblance and containment of documents , 1997, Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No.97TB100171).

[153]  Ngai Wong,et al.  MiSC: Mixed Strategies Crowdsourcing , 2019, IJCAI.

[154]  Vladimir Pestov.  Lower Bounds on Performance of Metric Tree Indexing Schemes for Exact Similarity Search in High Dimensions , 2012, Algorithmica.

[155]  Bernard Chazelle,et al.  Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform , 2006, STOC '06.

[156]  Parth Nagarkar,et al.  qwLSH: Cache-conscious Indexing for Processing Similarity Search Query Workloads in High-Dimensional Spaces , 2019, ICMR.

[157]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[158]  Christian S. Collberg,et al.  Repeatability in computer systems research , 2016, Commun. ACM.

[159]  Pavel Zezula,et al.  On the application of convex transforms to metric search , 2020, Pattern Recognit. Lett..

[160]  Ilya Mironov,et al.  On significance of the least significant bits for differential privacy , 2012, CCS.

[161]  Yunjun Gao,et al.  Pivot-based Metric Indexing , 2017, Proc. VLDB Endow..

[162]  Nicole Immorlica,et al.  Locality-sensitive hashing scheme based on p-stable distributions , 2004, SCG '04.

[163]  Lin Su,et al.  ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data , 2020, ArXiv.

[164]  Ilaria Bartolini,et al.  Query processing issues in region-based image databases , 2010, Knowledge and Information Systems.

[165]  Michael Stonebraker,et al.  Detecting Data Errors: Where are we and what needs to be done? , 2016, Proc. VLDB Endow..

[166]  M. Gromov.  Metric Structures for Riemannian and Non-Riemannian Spaces , 1999 .

[167]  Jiawei Han,et al.  CLARANS: A Method for Clustering Objects for Spatial Data Mining , 2002, IEEE Trans. Knowl. Data Eng..

[168]  B. S. Manjunath,et al.  Introduction to MPEG-7: Multimedia Content Description Interface , 2002 .

[169]  Francesco Silvestri,et al.  Similarity Search with Tensor Core Units , 2020, SISAP.

[170]  Kouichi Hirata,et al.  Pivot Selection for Dimension Reduction using Annealing by Increasing Resampling , 2017, LWDA.

[171]  Sanjeev Khanna,et al.  Why and Where: A Characterization of Data Provenance , 2001, ICDT.

[172]  Fabrizio Silvestri,et al.  How Random Walks Can Help Tourism , 2012, ECIR.

[173]  Charu C. Aggarwal,et al.  Data Mining: The Textbook , 2015 .

[174]  Mehdi Boukhechba,et al.  Prediction of next destinations from irregular patterns , 2018, J. Ambient Intell. Humaniz. Comput..

[175]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[176]  David Stutz,et al.  Neural Codes for Image Retrieval , 2015 .

[177]  Hans-Peter Kriegel,et al.  Fast Hierarchical Clustering Based on Compressed Data and OPTICS , 2000, PKDD.

[178]  Dominique Barth,et al.  PARINET: A tunable access method for in-network trajectories , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).

[179]  Shreyansh Gandhi,et al.  A Smart System for Selection of Optimal Product Images in E-Commerce , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[180]  Yao Wang,et al.  A robust and scalable clustering algorithm for mixed type attributes in large database environment , 2001, KDD '01.

[181]  Michael Isard,et al.  Object retrieval with large vocabularies and fast spatial matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[182]  Mayank Bawa,et al.  LSH forest: self-tuning indexes for similarity search , 2005, WWW '05.

[183]  Abbes Amira,et al.  Content-based image retrieval with compact deep convolutional features , 2017, Neurocomputing.

[184]  Johannes Gehrke,et al.  DEMON: Mining and Monitoring Evolving Data , 2001, IEEE Trans. Knowl. Data Eng..

[185]  Nieves R. Brisaboa,et al.  Indexing Dense Nested Metric Spaces for Efficient Similarity Search , 2009, Ershov Memorial Conference.

[186]  Michael E. Houle,et al.  Local Intrinsic Dimensionality II: Multivariate Analysis and Distributional Support , 2017, SISAP.

[187]  Pong C. Yuen,et al.  Very low resolution face recognition problem , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[188]  Arthur Zimek,et al.  On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study , 2016, Data Mining and Knowledge Discovery.

[189]  Michael Isard,et al.  Bundling features for large scale partial-duplicate web image search , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[190]  Erich Schubert,et al.  BETULA: Numerically Stable CF-Trees for BIRCH Clustering , 2020, SISAP.

[191]  James Ze Wang,et al.  SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[192]  David Novak,et al.  Speeding up Similarity Search by Sketches , 2016, SISAP.

[193]  Gonzalo Navarro,et al.  Metric Spaces Library , 2008 .

[194]  Gonzalo Navarro,et al.  Metric Databases , 2005, Encyclopedia of Database Technologies and Applications.

[195]  Jianmin Wang,et al.  Deep Visual-Semantic Quantization for Efficient Image Retrieval , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[196]  Carlos A. Coello Coello,et al.  Swarm Intelligence for Multi-objective Problems in Data Mining , 2009 .

[197]  Alexandros Nanopoulos,et al.  Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data , 2010, J. Mach. Learn. Res..

[198]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[199]  Richard C. H. Connor,et al.  Supermetric Search with the Four-Point Property , 2016, SISAP.

[200]  Ricardo A. Baeza-Yates,et al.  Searching in metric spaces , 2001, CSUR.

[201]  Takeshi Shinohara,et al.  On Dimension Reduction Mappings for Approximate Retrieval of Multi-dimensional Data , 1999, Progress in Discovery Science.

[202]  Tetsuji Kuboyama,et al.  Annealing by Increasing Resampling , 2019, ICPRAM.

[203]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[204]  Christian S. Jensen,et al.  PM-LSH , 2020, Proc. VLDB Endow..

[205]  Matthijs Douze,et al.  Searching in one billion vectors: Re-rank with source coding , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[206]  Heiko Schuldt,et al.  Searching in Video Collections Using Sketches and Sample Images - The Cineast System , 2016, MMM.

[207]  Gonzalo Navarro,et al.  Speeding up spatial approximation search in metric spaces , 2009, JEAL.

[208]  Piotr Indyk,et al.  Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality , 2012, Theory Comput..

[209]  Hisashi Koga,et al.  Fast Exact Algorithm to Solve Continuous Similarity Search for Evolving Queries , 2017, AIRS.

[210]  Wei Chu,et al.  Probabilistic Models for Incomplete Multi-dimensional Arrays , 2009, AISTATS.

[211]  Michael E. Houle,et al.  Effective and Efficient Algorithms for Flexible Aggregate Similarity Search in High Dimensional Spaces , 2015, IEEE Transactions on Knowledge and Data Engineering.

[212]  Subbarao Kambhampati,et al.  SourceRank: relevance and trust assessment for deep web sources based on inter-source agreement , 2010, WWW '10.

[213]  K. Selçuk Candan,et al.  Efficient Static and Dynamic In-Database Tensor Decompositions on Chunk-Based Array Stores , 2014, CIKM.

[214]  Anil K. Jain,et al.  Image retrieval using color and shape , 1996, Pattern Recognit..

[215]  Gustavo E. A. P. A. Batista,et al.  A Study of K-Nearest Neighbour as an Imputation Method , 2002, HIS.

[216]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[217]  Vitomir Struc,et al.  Adaptation of SIFT Features for Robust Face Recognition , 2010, ICIAR.

[218]  Qiong Wu,et al.  Locally private Jaccard similarity estimation , 2018, Concurr. Comput. Pract. Exp..

[219]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[220]  Stephan Eidenbenz,et al.  Online Dominating Set , 2016, SWAT.

[221]  Greg Hamerly,et al.  Geometric methods to accelerate k-means algorithms , 2016, SDM.

[222]  Shuicheng Yan,et al.  Sharing Residual Units Through Collective Tensor Factorization To Improve Deep Neural Networks , 2018, IJCAI.

[223]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[224]  D.M. Mount,et al.  An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[225]  Michael E. Houle,et al.  LID-Fingerprint: A Local Intrinsic Dimensionality-Based Fingerprinting Method , 2018, SISAP.

[226]  Xi Chen,et al.  Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization , 2010, SDM.

[227]  Philip S. Yu,et al.  Window-based Tensor Analysis on High-dimensional and Multi-aspect Streams , 2006, Sixth International Conference on Data Mining (ICDM'06).

[228]  Jinjun Xiong,et al.  Accelerating reduction and scan using tensor core units , 2018, ICS.

[229]  Tetsuji Kuboyama,et al.  Fast Filtering for Nearest Neighbor Search by Sketch Enumeration Without Using Matching , 2019, Australasian Conference on Artificial Intelligence.

[230]  D. Edwards.  The Structure of Superspace , 1975 .

[231]  Andrew P. Davison,et al.  Sumatra: A Toolkit for Reproducible Research , 2018, Implementing Reproducible Research.

[232]  Yury A. Malkov,et al.  Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[233]  Thomas Donaldson,et al.  An Ethical Algorithm , 1989 .

[234]  Sreenivas Gollapudi,et al.  Diversifying search results , 2009, WSDM '09.

[235]  Lars Grasedyck,et al.  F ¨ Ur Mathematik in Den Naturwissenschaften Leipzig a Projection Method to Solve Linear Systems in Tensor Format a Projection Method to Solve Linear Systems in Tensor Format , 2022 .

[236]  Hyunsoo Kim,et al.  Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method , 2008, SIAM J. Matrix Anal. Appl..

[237]  Pavel Zezula,et al.  D-Index: Distance Searching Index for Metric Data Sets , 2003, Multimedia Tools and Applications.

[238]  M. Hutson.  Artificial intelligence faces reproducibility crisis. , 2018, Science.

[239]  Greg Hamerly,et al.  Making k-means Even Faster , 2010, SDM.

[240]  Adrian E. Raftery,et al.  Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering , 2007, J. Classif..

[241]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[242]  K. Selçuk Candan,et al.  2PCP: Two-phase CP decomposition for billion-scale dense tensors , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[243]  Markus Kächele,et al.  Speeding up k-means by approximating Euclidean distances via block vectors , 2016, ICML.

[244]  Ken-ichi Kawarabayashi,et al.  Enhanced Estimation of Local Intrinsic Dimensionality Using Auxiliary Distances , 2016 .

[245]  Jeff Donahue,et al.  Visual Search at Pinterest , 2015, KDD.

[246]  Claudio Gennaro,et al.  Improving Multi-scale Face Recognition Using VGGFace2 , 2019, ICIAP Workshops.

[247]  Rasmus Bro,et al.  Multiway analysis of epilepsy tensors , 2007, ISMB/ECCB.

[248]  B. M. Hill,et al.  A Simple General Approach to Inference About the Tail of a Distribution , 1975 .

[249]  J. Hosking.  L‐Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics , 1990 .

[250]  Catherine C. McGeoch.  Experimental algorithmics , 2007, CACM.

[251]  Piotr Indyk,et al.  Edit Distance Cannot Be Computed in Strongly Subquadratic Time (unless SETH is false) , 2014, STOC.

[252]  Olga Sourina,et al.  Effective clustering and boundary detection algorithm based on Delaunay triangulation , 2008, Pattern Recognit. Lett..

[253]  Evgueni A. Haroutunian,et al.  Information Theory and Statistics , 2011, International Encyclopedia of Statistical Science.

[254]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[255]  Evaggelia Pitoura,et al.  Multiple Radii DisC Diversity: Result Diversification Based on Dissimilarity and Coverage , 2012, TODS.

[256]  Hanan Samet,et al.  Metric space similarity joins , 2008, TODS.

[257]  Andrzej Cichocki,et al.  Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions , 2016, Found. Trends Mach. Learn..

[258]  Gonzalo Navarro,et al.  Effective Proximity Retrieval by Ordering Permutations , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[259]  Raghu Ramakrishnan,et al.  Theory of nearest neighbors indexability , 2006, TODS.

[260]  Matthew O. Ward,et al.  An optimal strategy for monitoring top-k queries in streaming windows , 2011, EDBT/ICDT '11.

[261]  J. Ballani,et al.  Black box approximation of tensors in hierarchical Tucker format , 2013 .

[262]  Tim Kraska,et al.  The Case for Learned Index Structures , 2018 .

[263]  Wei Wang,et al.  I-LSH: I/O Efficient c-Approximate Nearest Neighbor Search in High-Dimensional Space , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).

[264]  Brigid Wilson,et al.  Implementing Reproducible Research , 2014 .

[265]  Aditya G. Parameswaran,et al.  Optimally Leveraging Density and Locality for Exploratory Browsing and Sampling , 2018, HILDA@SIGMOD.

[266]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[267]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[268]  Gonzalo Navarro.  Searching in metric spaces by spatial approximation , 2002, The VLDB Journal.

[269]  Christos Faloutsos,et al.  HaTen2: Billion-scale tensor decompositions , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[270]  Hisashi Kashima,et al.  Generalized Expansion Dimension , 2012, 2012 IEEE 12th International Conference on Data Mining Workshops.

[271]  David Novak,et al.  Designing Sketches for Similarity Filtering , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[272]  David Novak,et al.  Large-scale Image Retrieval using Neural Net Descriptors , 2015, SIGIR.

[273]  Eric P. Smith,et al.  An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.

[274]  Claudio A. Perez,et al.  Methodological improvement on local Gabor face recognition based on feature selection and enhanced Borda count , 2011, Pattern Recognit..

[275]  Beng Chin Ooi,et al.  BORDER: efficient computation of boundary points , 2006, IEEE Transactions on Knowledge and Data Engineering.

[276]  Tobias Christiani.  Fast Locality-Sensitive Hashing Frameworks for Approximate Near Neighbor Search , 2019, SISAP.

[277]  Clayton R. Pereira,et al.  Deep Learning-Aided Parkinson's Disease Diagnosis from Handwritten Dynamics , 2016, 2016 29th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI).

[278]  Theo Härder,et al.  Generalizing prefix filtering to improve set similarity joins , 2011, Inf. Syst..

[279]  M. Hazelton.  Variable kernel density estimation , 2003 .

[280]  Junfeng He,et al.  Optimal Parameters for Locality-Sensitive Hashing , 2012, Proceedings of the IEEE.

[281]  Jian-Jia Chen,et al.  Using a Set of Triangle Inequalities to Accelerate K-means Clustering , 2020, SISAP.

[282]  Jorge Urrutia,et al.  Half-Space Proximal: A New Local Test for Extracting a Bounded Dilation Spanner of a Unit Disk Graph , 2005, OPODIS.

[283]  Tomás Skopal,et al.  Approximate search in dissimilarity spaces using GA , 2019, GECCO.

[284]  Heiko Schuldt,et al.  Cottontail DB: An Open Source Database System for Multimedia Retrieval and Analysis , 2020, ACM Multimedia.

[285]  Zhe Wang,et al.  Modeling LSH for performance tuning , 2008, CIKM '08.

[286]  K. Selçuk Candan,et al.  BICP: Block-Incremental CP Decomposition with Update Sensitive Refinement , 2016, CIKM.

[287]  Pasi Fränti,et al.  K-means properties on six clustering benchmark datasets , 2018, Applied Intelligence.

[288]  Rasmus Pagh,et al.  I/O-Efficient Similarity Join , 2015, ESA.

[289]  Wei Tsang Ooi,et al.  Hierarchical, non-uniform locality sensitive hashing and its application to video identification , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[290]  Divesh Srivastava,et al.  On query result diversification , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[291]  Hans-Peter Kriegel,et al.  Angle-based outlier detection in high-dimensional data , 2008, KDD.

[292]  Bo Yuan,et al.  A highly scalable clustering scheme using boundary information , 2017, Pattern Recognit. Lett..

[293]  Robert F. Sproull,et al.  Refinements to nearest-neighbor searching ink-dimensional trees , 1991, Algorithmica.

[294]  Nikos Mamoulis,et al.  Spatio-textual similarity joins , 2012, Proc. VLDB Endow..

[295]  Michael E. Houle,et al.  Efficient Algorithms for Similarity Search in Axis-Aligned Subspaces , 2014, SISAP.

[296]  Chen Li,et al.  Efficient parallel set-similarity joins using MapReduce , 2010, SIGMOD Conference.

[297]  Arcwise Isometries,et al.  A Course in Metric Geometry , 2001 .

[298]  Arthur Zimek,et al.  Subspace Determination Through Local Intrinsic Dimensional Decomposition , 2019, SISAP.

[299]  Alexander Novikov,et al.  Tensorizing Neural Networks , 2015, NIPS.

[300]  Lee Sael,et al.  SCouT: Scalable coupled matrix-tensor factorization - algorithm and discoveries , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[301]  B. Reilly.  Social Choice in the South Seas: Electoral Innovation and the Borda Count in the Pacific Island Countries , 2002 .

[302]  Yue Zhao,et al.  Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup , 2015, ICML.

[303]  Takanori Maehara,et al.  On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm , 2017, NIPS.

[304]  Christoph Zauner,et al.  Implementation and Benchmarking of Perceptual Image Hash Functions , 2010 .

[305]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[306]  Raffaele Perego,et al.  Expansion via Prediction of Importance with Contextualization , 2020, SIGIR.

[307]  Martin Aumüller,et al.  ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms , 2018, SISAP.

[308]  Aoying Zhou,et al.  Density-Based Clustering over an Evolving Data Stream with Noise , 2006, SDM.

[309]  Ivan Oseledets,et al.  Tensor-Train Decomposition , 2011, SIAM J. Sci. Comput..

[310]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[311]  Andrew W. Moore,et al.  Accelerating exact k-means algorithms with geometric reasoning , 1999, KDD '99.

[312]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[313]  Jing Lin,et al.  An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection , 2015, Reliab. Eng. Syst. Saf..

[314]  Andrew W. Fitzgibbon,et al.  Efficient Object Category Recognition Using Classemes , 2010, ECCV.

[315]  Milan Straka,et al.  Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe , 2017, CoNLL.

[316]  Li Fei-Fei,et al.  CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[317]  Ira Assent,et al.  The ClusTree: indexing micro-clusters for anytime stream mining , 2011, Knowledge and Information Systems.

[318]  Peter N. Yianilos,et al.  Data structures and algorithms for nearest neighbor search in general metric spaces , 1993, SODA '93.

[319]  Josh Alman,et al.  Limits on the Universal method for matrix multiplication , 2018, CCC.

[320]  Tian Zhang,et al.  BIRCH: A New Data Clustering Algorithm and Its Applications , 1997, Data Mining and Knowledge Discovery.

[321]  Flavio Vella,et al.  A Computational Model for Tensor Core Units , 2019, SPAA.

[322]  K. Scharnhorst,et al.  Angles in Complex Vector Spaces , 1999 .

[323]  Ralph Gasser,et al.  Interactive Search or Sequential Browsing? A Detailed Analysis of the Video Browser Showdown 2018 , 2019, ACM Trans. Multim. Comput. Commun. Appl..

[324]  Xuemin Lin,et al.  SKYPE: Top-k Spatial-keyword Publish/Subscribe Over Sliding Window , 2016, Proc. VLDB Endow..

[325]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[326]  Razvan Pascanu,et al.  A simple neural network module for relational reasoning , 2017, NIPS.

[327]  Arthur Zimek,et al.  On the Correlation Between Local Intrinsic Dimensionality and Outlierness , 2018, SISAP.

[328]  Carl Boettiger,et al.  An introduction to Docker for reproducible research , 2014, OPSR.

[329]  Facundo Mémoli,et al.  Gromov–Wasserstein Distances and the Metric Approach to Object Matching , 2011, Found. Comput. Math..

[330]  Jonathan Goldstein,et al.  When Is ''Nearest Neighbor'' Meaningful? , 1999, ICDT.

[331]  Martin Aumüller,et al.  The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search , 2019, SISAP.

[332]  Reza Zafarani,et al.  Users joining multiple sites: Friendship and popularity variations across sites , 2016, Inf. Fusion.

[333]  Martin Necaský,et al.  Explainable Similarity of Datasets Using Knowledge Graph , 2019, SISAP.

[334]  Bolei Zhou,et al.  Learning Deep Features for Scene Recognition using Places Database , 2014, NIPS.

[335]  Vladimir Pestov,et al.  Curse of Dimensionality in Pivot Based Indexes , 2009, 2009 Second International Workshop on Similarity Search and Applications.

[336]  Heiko Schuldt,et al.  Interactive Lifelog Retrieval with vitrivr , 2020, LSC@ICMR.

[337]  Yihua Tan,et al.  Airport Detection From Large IKONOS Images Using Clustered SIFT Keypoints and Region Information , 2011, IEEE Geoscience and Remote Sensing Letters.

[338]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[339]  L. Hunt.  Missing Data Imputation and Its Effect on the Accuracy of Classification , 2017 .

[340]  Richard Ryan Williams,et al.  Distributed PCP Theorems for Hardness of Approximation in P , 2017, 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS).

[341]  Michael E. Houle,et al.  The Influence of Hubness on NN-Descent , 2019, Int. J. Artif. Intell. Tools.

[342]  Ulf Leser,et al.  Set Similarity Joins on MapReduce: An Experimental Survey , 2018, Proc. VLDB Endow..

[343]  Facundo Mémoli,et al.  Eurographics Symposium on Point-based Graphics (2007) on the Use of Gromov-hausdorff Distances for Shape Comparison , 2022 .

[344]  Rasmus Pagh,et al.  Oblivious Sketching of High-Degree Polynomial Kernels , 2020, SODA.

[345]  Christian S. Jensen,et al.  SWOOP: Top-k Similarity Joins over Set Streams , 2017, ArXiv.

[346]  Anastasios Tefas,et al.  Deep convolutional learning for Content Based Image Retrieval , 2018, Neurocomputing.

[347]  Martin Aumüller,et al.  Running Experiments with Confidence and Sanity , 2020, SISAP.

[348]  Richard C. H. Connor,et al.  Hilbert Exclusion , 2016, ACM Trans. Inf. Syst..

[349]  Jianqing Fan,et al.  Distributions of angles in random packing on spheres , 2013, J. Mach. Learn. Res..

[350]  Jakub Lokoc,et al.  NM-Tree: Flexible Approximate Similarity Search in Metric and Non-metric Spaces , 2008, DEXA.

[351]  Agma J. M. Traina,et al.  Querying on large and complex databases by content: Challenges on variety and veracity regarding real applications , 2019, Inf. Syst..

[352]  Fabrizio Falchi,et al.  Cross-Resolution Learning for Face Recognition , 2020, Image Vis. Comput..

[353]  K. Selçuk Candan,et al.  Focusing Decomposition Accuracy by Personalizing Tensor Decomposition (PTD) , 2014, CIKM.

[354]  K. Selçuk Candan,et al.  Decomposition-by-normalization (DBN): leveraging approximate functional dependencies for efficient CP and tucker decompositions , 2015, Data Mining and Knowledge Discovery.

[355]  A. Blumberg,et al.  Topological Data Analysis for Genomics and Evolution: Topology in Biology , 2020 .

[356]  Tian Zhang.  Data clustering for very large datasets plus applications , 1997 .

[357]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[358]  James Bailey,et al.  Dimensionality-Driven Learning with Noisy Labels , 2018, ICML.

[359]  Alastair Scott,et al.  On the Asymptotic Distribution of Ratio and Regression Estimators , 1981 .

[360]  Tian Zhang,et al.  Fast density estimation using CF-kernel for very large databases , 1999, KDD '99.

[361]  Philip S. Yu,et al.  A Framework for Clustering Evolving Data Streams , 2003, VLDB.

[362]  Yuto Yamaguchi,et al.  Tensor Decomposition with Missing Indices , 2017, IJCAI.

[363]  Henry D. Shapiro,et al.  Algorithms and Experiments: The New (and Old) Methodology , 2001, J. Univers. Comput. Sci..

[364]  Xiaojie Li,et al.  Angle-Based Outlier Detection Algorithm with More Stable Relationships , 2015 .

[365]  Andrea Esuli,et al.  Use of permutation prefixes for efficient and scalable approximate similarity search , 2012, Inf. Process. Manag..

[366]  Gr'egory Miermont,et al.  Tessellations of random maps of arbitrary genus , 2007, 0712.3688.

[367]  Magnus Lie Hetland.  The Basic Principles of Metric Indexing , 2009 .

[368]  Yufei Tao,et al.  DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation , 2015, SIGMOD Conference.

[369]  Michael E. Houle,et al.  Improving k-NN Graph Accuracy Using Local Intrinsic Dimensionality , 2017, SISAP.

[370]  Jianmin Wang,et al.  Deep Cauchy Hashing for Hamming Space Retrieval , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[371]  Zhe Wang,et al.  Intelligent Probing for Locality Sensitive Hashing: Multi-Probe LSH and Beyond , 2017, Proc. VLDB Endow..

[372]  Philip Levis,et al.  Locality-Sensitive Hashing for Earthquake Detection: A Case Study Scaling Data-Driven Science , 2018, Proc. VLDB Endow..

[373]  Kai Li,et al.  Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces , 2008, SIGIR '08.

[374]  Kouichi Hirata,et al.  Fast Nearest Neighbor Search with Narrow 16-bit Sketch , 2019, ICPRAM.

[375]  Cláudio T. Silva,et al.  VisTrails: visualization meets data management , 2006, SIGMOD Conference.

[376]  Michael Gertz,et al.  Numerically stable parallel computation of (co-)variance , 2018, SSDBM.

[377]  Houjun Tang,et al.  Multivariate regression for pivot selection: A preliminary study , 2011, 2011 3rd Symposium on Web Society.

[378]  Claudio Gennaro,et al.  Learning visual features for relational CBIR , 2019, International Journal of Multimedia Information Retrieval.

[379]  Zhe Wang,et al.  Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search , 2007, VLDB.

[380]  Hui Xiong,et al.  Time-aware metric embedding with asymmetric projection for successive POI recommendation , 2018, World Wide Web.

[381]  Gonzalo Navarro,et al.  A compact space decomposition for effective metric indexing , 2005, Pattern Recognit. Lett..

[382]  Alexander J. Smola,et al.  Stacked Attention Networks for Image Question Answering , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[383]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[384]  Radford M. Neal.  Probabilistic Inference Using Markov Chain Monte Carlo Methods , 2011 .

[385]  David A. Patterson,et al.  A domain-specific architecture for deep neural networks , 2018, Commun. ACM.

[386]  Fabrizio Silvestri,et al.  On Learning Prediction Models for Tourists Paths , 2015, ACM Trans. Intell. Syst. Technol..

[387]  Deirdre K. Mulligan,et al.  Differential Privacy in Practice: Expose your Epsilons! , 2019, J. Priv. Confidentiality.

[388]  Wellington Santos Martins,et al.  Efficient Filter-Based Algorithms for Exact Set Similarity Join on GPUs , 2017, ICEIS.

[389]  Dennis Shasha,et al.  ReproZip: The Reproducibility Packer , 2016, J. Open Source Softw..

[390]  W. B. Johnson,et al.  Extensions of Lipschitz mappings into Hilbert space , 1984 .

[391]  Alexandr Andoni,et al.  Optimal Data-Dependent Hashing for Approximate Near Neighbors , 2015, STOC.

[392]  Vladimir Estivill-Castro,et al.  Why so many clustering algorithms: a position paper , 2002, SKDD.

[393]  Shima Moghtasedi.  Time-Based Similar Trajectories on Graphs , 2018, ICTCS.

[394]  Christos Faloutsos,et al.  GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries , 2012, KDD.

[395]  Thomas Bartz-Beielstein,et al.  Experimental Analysis of Optimization Algorithms: Tuning and Beyond , 2014, Theory and Principled Methods for the Design of Metaheuristics.

[396]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[397]  David Novak,et al.  Improving Sketches for Similarity Search , 2015 .

[398]  Huan Liu,et al.  Feature selection for clustering - a filter solution , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[399]  Daniel M. Kane,et al.  Sparser Johnson-Lindenstrauss Transforms , 2010, JACM.

[400]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[401]  Sergei Vassilvitskii,et al.  k-means++: the advantages of careful seeding , 2007, SODA '07.

[402]  Ricardo A. Baeza-Yates,et al.  Proximity Matching Using Fixed-Queries Trees , 1994, CPM.

[403]  Cordelia Schmid,et al.  Product Quantization for Nearest Neighbor Search , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[404]  Claudio Gennaro,et al.  Deep Permutations: Deep Convolutional Neural Networks and Permutation-Based Indexing , 2016, SISAP.

[405]  Kyriakos Mouratidis,et al.  Continuous Top-k Monitoring on Document Streams , 2017, IEEE Transactions on Knowledge and Data Engineering.

[406]  Roberto J. Bayardo,et al.  Scaling up all pairs similarity search , 2007, WWW '07.

[407]  Davide Martinenghi,et al.  A k-Skyband Approach for Feature Selection , 2019, SISAP.

[408]  Anita Winter,et al.  Invariance principle for variable speed random walks on trees , 2017 .

[409]  Rasmus Pagh,et al.  A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data , 2012, KDD.

[410]  Zane T. Smith,et al.  Gromov-Hausdorff distances on $p$-metric spaces and ultrametric spaces , 2019 .

[411]  Soumen Chakrabarti,et al.  Dynamic personalized pagerank in entity-relation graphs , 2007, WWW '07.

[412]  Alessandro Panconesi,et al.  Concentration of Measure for the Analysis of Randomized Algorithms , 2009 .

[413]  Magnus Lie Hetland,et al.  Indexing inexact proximity search with distance regression in pivot space , 2010, SISAP.

[414]  K. Selçuk Candan,et al.  M2TD: Multi-Task Tensor Decomposition for Sparse Ensemble Simulations , 2018, 2018 IEEE 34th International Conference on Data Engineering (ICDE).

[415]  Paul S. Bradley,et al.  Scaling Clustering Algorithms to Large Databases , 1998, KDD.

[416]  Rasmus Bro,et al.  The N-way Toolbox for MATLAB , 2000 .

[417]  Tomás Skopal,et al.  Analysing Indexability of Intrinsically High-Dimensional Data Using TriGen , 2020, SISAP.

[418]  Pavel Zezula,et al.  Separable Splits of Metric Data Sets , 2001, SEBD.

[419]  James Bailey,et al.  Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality , 2018, ArXiv.

[420]  Charles Elkan,et al.  Using the Triangle Inequality to Accelerate k-Means , 2003, ICML.

[421]  Nikolaus Augsten,et al.  An Empirical Evaluation of Set Similarity Join Techniques , 2016, Proc. VLDB Endow..

[422]  Pradeep Dubey,et al.  Streaming Similarity Search over one Billion Tweets using Parallel Locality-Sensitive Hashing , 2013, Proc. VLDB Endow..

[423]  Zi Huang,et al.  SK-LSH: An Efficient Index Structure for Approximate Nearest Neighbor Search , 2014, Proc. VLDB Endow..

[424]  Jakub Lokoc,et al.  SOM-Hunter: Video Browsing with Relevance-to-SOM Feedback Loop , 2020, MMM.

[425]  Bi-Ru Dai,et al.  Accelerating K-Means by Grouping Points Automatically , 2017, DaWaK.

[426]  Philip S. Yu,et al.  A Framework for Projected Clustering of High Dimensional Data Streams , 2004, VLDB.

[427]  Jianzhong Li,et al.  A survey of query result diversification , 2017, Knowledge and Information Systems.

[428]  Ruslan Salakhutdinov,et al.  Probabilistic Matrix Factorization , 2007, NIPS.

[429]  Omkar M. Parkhi,et al.  VGGFace2: A Dataset for Recognising Faces across Pose and Age , 2017, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).

[430]  Paolo Papotti,et al.  RuleMiner: Data quality rules discovery , 2014, 2014 IEEE 30th International Conference on Data Engineering.

[431]  Stefan Lee,et al.  ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks , 2019, NeurIPS.

[432]  Walter A. Burkhard,et al.  Some approaches to best-match file searching , 1973, Commun. ACM.

[433]  Igor Vajda,et al.  On Divergences and Informations in Statistics and Information Theory , 2006, IEEE Transactions on Information Theory.

[434]  Kouichi Hirata,et al.  Nearest Neighbor Search using Sketches as Quantized Images of Dimension Reduction , 2018, ICPRAM.

[435]  Hiroyuki Kitagawa,et al.  Continuous Angle-based Outlier Detection on High-dimensional Data Streams , 2015, IDEAS.

[436]  Mikkel Thorup,et al.  Confirmation Sampling for Exact Nearest Neighbor Search , 2020, SISAP.

[437]  D. Sculley,et al.  Web-scale k-means clustering , 2010, WWW '10.

[438]  Roberto Grossi,et al.  Finding Structurally and Temporally Similar Trajectories in Graphs , 2020, SEA.

[439]  Arnab Bhattacharya,et al.  HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces , 2018, Proc. VLDB Endow..

[440]  Felix Schmiedl,et al.  Computational Aspects of the Gromov–Hausdorff Distance and its Application in Non-rigid Shape Matching , 2017, Discret. Comput. Geom..

[441]  Ninghui Li,et al.  Locally Differentially Private Protocols for Frequency Estimation , 2017, USENIX Security Symposium.

[442]  Zhe Wang,et al.  Sizing sketches: a rank-based analysis for similarity search , 2007, SIGMETRICS '07.

[443]  Peter Young,et al.  From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions , 2014, TACL.

[444]  Ernst J. Rummeny,et al.  Lumbar muscle and vertebral bodies segmentation of chemical shift encoding-based water-fat MRI: the reference database MyoSegmenTUM spine , 2019, BMC Musculoskeletal Disorders.

[445]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[446]  François Fleuret,et al.  Fast k-means with accurate bounds , 2016, ICML.

[447]  Raffaele Perego,et al.  Efficient Document Re-Ranking for Transformers by Precomputing Term Representations , 2020, SIGIR.

[448]  Jing Wang,et al.  Fast approximate k-means via cluster closures , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[449]  Pavel Zezula,et al.  Evaluation Platform for Content-Based Image Retrieval Systems , 2011, TPDL.

[450]  Craig MacDonald,et al.  Sparse Spatial Selection for Novelty-Based Search Result Diversification , 2011, SPIRE.

[451]  Vanessa Sochat,et al.  Singularity: Scientific containers for mobility of compute , 2017, PloS one.

[452]  Stéphane Marchand-Maillet,et al.  Indexability-Based Dataset Partitioning , 2019, SISAP.

[453]  Tomás Skopal,et al.  Non-metric Similarity Search Using Genetic TriGen , 2019, SISAP.

[454]  Alexandr Andoni,et al.  Optimal Hashing-based Time-Space Trade-offs for Approximate Near Neighbors , 2016, SODA.

[455]  Viktor Leis,et al.  Query optimization through the looking glass, and what we found running the Join Order Benchmark , 2017, The VLDB Journal.

[456]  Jonathan Drake.  Faster k-means clustering. , 2013 .

[457]  Michael E. Houle,et al.  Dimensionality, Discriminability, Density and Distance Distributions , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

[458]  Agma J. M. Traina,et al.  Exploring Diversified Similarity with Kundaha , 2018, CIKM.

[459]  David Novak,et al.  Building a web-scale image similarity search system , 2010, Multimedia Tools and Applications.

[460]  Jeffrey Xu Yu,et al.  Efficient similarity joins for near-duplicate detection , 2011, TODS.