A comprehensive empirical comparison of hubness reduction in high-dimensional spaces
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[1] Marco Saerens,et al. Centering Similarity Measures to Reduce Hubs , 2013, EMNLP.
[2] Arthur Flexer,et al. Mutual proximity graphs for improved reachability in music recommendation , 2017, Journal of new music research.
[3] Arthur Flexer,et al. Improving Visualization of High-Dimensional Music Similarity Spaces , 2015, ISMIR.
[4] Adam Powell,et al. The Origins of Lactase Persistence in Europe , 2009, PLoS Comput. Biol..
[5] Arthur Flexer,et al. HUBNESS-AWARE OUTLIER DETECTION FOR MUSIC GENRE RECOGNITION , 2016 .
[6] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.
[7] Arthur Flexer,et al. Centering Versus Scaling for Hubness Reduction , 2016, ICANN.
[8] Alexandr Andoni,et al. Practical and Optimal LSH for Angular Distance , 2015, NIPS.
[9] Peter Knees,et al. Improving Neighborhood-Based Collaborative Filtering by Reducing Hubness , 2014, ICMR.
[10] Brian Kulis,et al. Metric Learning: A Survey , 2013, Found. Trends Mach. Learn..
[11] H. A. Guvenir,et al. A supervised machine learning algorithm for arrhythmia analysis , 1997, Computers in Cardiology 1997.
[12] Peter J. Bickel,et al. Maximum Likelihood Estimation of Intrinsic Dimension , 2004, NIPS.
[13] Dunja Mladenic,et al. The influence of hubness on nearest-neighbor methods in object recognition , 2011, 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing.
[14] Andrew McCallum,et al. A comparison of event models for naive bayes text classification , 1998, AAAI 1998.
[15] Arthur Flexer,et al. Can Shared Nearest Neighbors Reduce Hubness in High-Dimensional Spaces? , 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.
[16] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[17] Zongkai Yang,et al. Variable Length Character N-Gram Approach for Online Writeprint Identification , 2010, 2010 International Conference on Multimedia Information Networking and Security.
[18] Arthur Flexer,et al. The unbalancing effect of hubs on K-medoids clustering in high-dimensional spaces , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[19] Alexandros Nanopoulos,et al. Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection , 2015, IEEE Transactions on Knowledge and Data Engineering.
[20] Fabio Pagnotta,et al. USING DATA MINING TO PREDICT SECONDARY SCHOOL STUDENT ALCOHOL CONSUMPTION , 2016 .
[21] Arthur Flexer,et al. The relation of hubs to the Doddington zoo in speaker verification , 2013, 21st European Signal Processing Conference (EUSIPCO 2013).
[22] Dunja Mladenic,et al. The Role of Hubness in Clustering High-Dimensional Data , 2011, IEEE Transactions on Knowledge and Data Engineering.
[23] Markus Schedl,et al. Local and global scaling reduce hubs in space , 2012, J. Mach. Learn. Res..
[24] Chris Mesterharm,et al. Active learning using on-line algorithms , 2011, KDD.
[25] Ricardo Chavarriaga,et al. The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition , 2013, Pattern Recognit. Lett..
[26] Cheng Soon Ong,et al. Multivariate spearman's ρ for aggregating ranks using copulas , 2016 .
[27] Peter Kokol,et al. Stability of Ranked Gene Lists in Large Microarray Analysis Studies , 2010, Journal of biomedicine & biotechnology.
[28] Gavin C. Cawley,et al. On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation , 2010, J. Mach. Learn. Res..
[29] Bernd Bischl,et al. mlr: Machine Learning in R , 2016, J. Mach. Learn. Res..
[30] Dunja Mladenic,et al. Hubness-aware shared neighbor distances for high-dimensional $$k$$-nearest neighbor classification , 2014, Knowledge and Information Systems.
[31] Antonino Staiano,et al. Intrinsic dimension estimation: Advances and open problems , 2016, Inf. Sci..
[32] Luís Torgo,et al. OpenML: networked science in machine learning , 2014, SKDD.
[33] Arthur Flexer. An Empirical Analysis of Hubness in Unsupervised Distance-Based Outlier Detection , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).
[34] Elias Oliveira,et al. An Evolving System Based on Probabilistic Neural Network , 2010, 2010 Eleventh Brazilian Symposium on Neural Networks.
[35] Michel Verleysen,et al. The Concentration of Fractional Distances , 2007, IEEE Transactions on Knowledge and Data Engineering.
[36] Piotr Indyk,et al. Approximate nearest neighbors: towards removing the curse of dimensionality , 1998, STOC '98.
[37] Cordelia Schmid,et al. A contextual dissimilarity measure for accurate and efficient image search , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[38] Alexandros Nanopoulos,et al. Nearest neighbor regression in the presence of bad hubs , 2015, Knowl. Based Syst..
[39] Fikret S. Gürgen,et al. Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings , 2013, IEEE Journal of Biomedical and Health Informatics.
[40] C. Borgelt,et al. The Hubness Phenomenon: Fact or Artifact? , 2013 .
[41] Arthur Flexer,et al. Choosing ℓp norms in high-dimensional spaces based on hub analysis , 2015, Neurocomputing.
[42] Emmanuel Vincent,et al. An investigation of likelihood normalization for robust ASR , 2014, INTERSPEECH.
[43] Arthur Flexer,et al. A Case for Hubness Removal in High-Dimensional Multimedia Retrieval , 2014, ECIR.
[44] Gholamreza Haffari,et al. Data-dependent dissimilarity measure: an effective alternative to geometric distance measures , 2017, Knowledge and Information Systems.
[45] Richard Bellman,et al. Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.
[46] Peter Kaiser,et al. Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning , 2009, PLoS Comput. Biol..
[47] Pietro Perona,et al. Self-Tuning Spectral Clustering , 2004, NIPS.
[48] Kenji Fukumizu,et al. Reducing Hubness: A Cause of Vulnerability in Recommender Systems , 2015, SIGIR.
[49] K. Cios,et al. Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome , 2015, PloS one.
[50] Nenad Tomasev. Taming the Empirical Hubness Risk in Many Dimensions , 2015, SDM.
[51] Yiming Yang,et al. RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..
[52] Markus Schedl,et al. Using Mutual Proximity to Improve Content-Based Audio Similarity , 2011, ISMIR.
[53] Ray A. Jarvis,et al. Clustering Using a Similarity Measure Based on Shared Near Neighbors , 1973, IEEE Transactions on Computers.
[54] Kenji Fukumizu,et al. Localized Centering: Reducing Hubness in Large-Sample Data , 2015, AAAI.
[55] Kenji Fukumizu,et al. Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness , 2016, AAAI.
[56] François Pachet,et al. Improving Timbre Similarity : How high’s the sky ? , 2004 .
[57] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[58] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[59] Alexandros Nanopoulos,et al. Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data , 2010, J. Mach. Learn. Res..
[60] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[61] Jung-Ying Wang,et al. Application of Support Vector Machines in Bioinformatics , 2002 .