Archetype analysis: A new subspace outlier detection approach

[1]  M. Kouchi Anthropometric methods for apparel design: Body measurement devices and techniques , 2020, Anthropometry, Apparel Sizing and Design.

[2]  Irene Epifanio,et al.  Detection of Anomalies in Water Networks by Functional Data Analysis , 2018, Mathematical Problems in Engineering.

[3]  Pang-Ning Tan,et al.  Outrank: a Graph-Based Outlier Detection Framework Using Random Walk , 2008, Int. J. Artif. Intell. Tools.

[4]  Zengyou He,et al.  Discovering cluster-based local outliers , 2003, Pattern Recognit. Lett..

[5]  Pang-Ning Tan,et al.  Outlier Detection Using Random Walks , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).

[6]  Haibo He,et al.  A local density-based approach for outlier detection , 2017, Neurocomputing.

[7]  T. Horstmann,et al.  Sex-related differences in foot shape of adult Caucasians – a follow-up study focusing on long and short feet , 2011, Ergonomics.

[8]  Morten Mørup,et al.  Archetypal Analysis for Modeling Multisubject fMRI Data , 2016, IEEE Journal of Selected Topics in Signal Processing.

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

[10]  L. Alegre,et al.  Foot morphology in Spanish school children according to sex and age , 2014, Ergonomics.

[11]  Ian Davidson,et al.  Discovering Contexts and Contextual Outliers Using Random Walks in Graphs , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[12]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[13]  Irene Epifanio,et al.  Archetypoid analysis for sports analytics , 2017, Data Mining and Knowledge Discovery.

[14]  Jian Tang,et al.  Enhancing Effectiveness of Outlier Detections for Low Density Patterns , 2002, PAKDD.

[15]  T. Okura,et al.  Gender differences of foot characteristics in older Japanese adults using a 3D foot scanner , 2015, Journal of Foot and Ankle Research.

[16]  Giancarlo Ragozini,et al.  Interval Archetypes: A New Tool for Interval Data Analysis , 2012, Stat. Anal. Data Min..

[17]  Zaïd Harchaoui,et al.  Fast and Robust Archetypal Analysis for Representation Learning , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Robert Sitnik,et al.  3D anthropometric algorithms for the estimation of measurements required for specialized garment design , 2017, Expert Syst. Appl..

[19]  Irene Epifanio,et al.  Robust multivariate and functional archetypal analysis with application to financial time series analysis , 2018, Physica A: Statistical Mechanics and its Applications.

[20]  Zhen Liu,et al.  An Optimized Computational Framework for Isolation Forest , 2018 .

[21]  Irene Epifanio,et al.  Robust archetypoids for anomaly detection in big functional data , 2020, Adv. Data Anal. Classif..

[22]  Guillermo Ayala,et al.  Apparel sizing using trimmed PAM and OWA operators , 2012, Expert Syst. Appl..

[23]  Guillermo Vinué,et al.  Anthropometry: An R Package for Analysis of Anthropometric Data , 2017 .

[24]  Sandra Alemany,et al.  Archetypoids: A new approach to define representative archetypal data , 2015, Comput. Stat. Data Anal..

[25]  Sandra Alemany,et al.  An ensemble of ordered logistic regression and random forest for child garment size matching , 2016, Comput. Ind. Eng..

[26]  Bo Zong,et al.  Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection , 2018, ICLR.

[27]  Amelia Simó,et al.  Archetypal shapes based on landmarks and extension to handle missing data , 2018, Adv. Data Anal. Classif..

[28]  C. Ji An Archetypal Analysis on , 2005 .

[29]  Hans-Peter Kriegel,et al.  Interpreting and Unifying Outlier Scores , 2011, SDM.

[30]  Irene Epifanio,et al.  ARCHETYPAL ANALYSIS: AN ALTERNATIVE TO CLUSTERING FOR UNSUPERVISED TEXTURE SEGMENTATION , 2019, Image Analysis & Stereology.

[31]  Markus Scholz,et al.  Reliability of 3D laser-based anthropometry and comparison with classical anthropometry , 2016, Scientific Reports.

[32]  Manuel J. A. Eugster,et al.  From Spider-man to Hero - archetypal analysis in R , 2009 .

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

[34]  Sandra Alemany,et al.  Archetypal analysis: Contributions for estimating boundary cases in multivariate accommodation problem , 2013, Comput. Ind. Eng..

[35]  Manuel J. A. Eugster,et al.  Weighted and robust archetypal analysis , 2011, Comput. Stat. Data Anal..

[36]  Irene Epifanio,et al.  Functional archetype and archetypoid analysis , 2016, Comput. Stat. Data Anal..

[37]  Vivekanand Gopalkrishnan,et al.  Mining Outliers with Ensemble of Heterogeneous Detectors on Random Subspaces , 2010, DASFAA.

[38]  Igor Kononenko,et al.  Weighted archetypal analysis of the multi-element graph for query-focused multi-document summarization , 2014, Expert Syst. Appl..

[39]  Christian Bauckhage,et al.  Descriptive matrix factorization for sustainability Adopting the principle of opposites , 2011, Data Mining and Knowledge Discovery.

[40]  Wei Sun,et al.  Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network , 2019, KDD.

[41]  USAD , 2020, Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

[42]  Morten Mørup,et al.  Archetypal analysis of diverse Pseudomonas aeruginosa transcriptomes reveals adaptation in cystic fibrosis airways , 2013, BMC Bioinformatics.

[43]  Irene Epifanio,et al.  Finding archetypal patterns for binary questionnaires , 2020 .

[44]  Zhen Liu,et al.  VOS: A new outlier detection model using virtual graph , 2019, Knowl. Based Syst..

[45]  Bell Telephone,et al.  ROBUST ESTIMATES, RESIDUALS, AND OUTLIER DETECTION WITH MULTIRESPONSE DATA , 1972 .

[46]  I. Epifanio,et al.  Forecasting basketball players' performance using sparse functional data , 2019, Stat. Anal. Data Min..

[47]  Ulf Brefeld,et al.  Frame-based Data Factorizations , 2017, ICML.

[48]  Tyler Davis,et al.  Memory for Category Information Is Idealized Through Contrast With Competing Options , 2010, Psychological science.

[49]  Hongxing He,et al.  A comparative study of RNN for outlier detection in data mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[50]  Seiichi Uchida,et al.  A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data , 2016, PloS one.

[51]  Hans-Peter Kriegel,et al.  LoOP: local outlier probabilities , 2009, CIKM.

[52]  Zhi-Hua Zhou,et al.  Isolation Forest , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[53]  Heecheon You,et al.  Evaluation of the multivariate accommodation performance of the grid method. , 2010, Applied ergonomics.

[54]  Amelia Simó,et al.  A data-driven classification of 3D foot types by archetypal shapes based on landmarks , 2020, PloS one.

[55]  Sohan Seth,et al.  Probabilistic archetypal analysis , 2013, Machine Learning.

[56]  Lefteris Angelis,et al.  A novel single-trial methodology for studying brain response variability based on archetypal analysis , 2015, Expert Syst. Appl..

[57]  Manuel J. A. Eugster,et al.  Performance Profiles based on Archetypal Athletes , 2012 .

[58]  Giancarlo Ragozini,et al.  On the use of archetypes as benchmarks , 2008 .

[59]  Amelia Simó,et al.  Archetypal Analysis With Missing Data: See All Samples by Looking at a Few Based on Extreme Profiles , 2020, The American Statistician.

[60]  F. Palumbo,et al.  Archetypal analysis for data‐driven prototype identification , 2017, Stat. Anal. Data Min..

[61]  Lars Kai Hansen,et al.  Archetypal analysis for machine learning , 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

[62]  Eduardo Parrilla,et al.  3D Body Modelling and Applications , 2018 .

[63]  Anthony K. H. Tung,et al.  Ranking Outliers Using Symmetric Neighborhood Relationship , 2006, PAKDD.

[64]  Maurizio Filippone,et al.  A comparative evaluation of outlier detection algorithms: Experiments and analyses , 2018, Pattern Recognit..

[65]  Slim Abdennadher,et al.  Enhancing one-class support vector machines for unsupervised anomaly detection , 2013, ODD '13.

[66]  Christos Faloutsos,et al.  LOCI: fast outlier detection using the local correlation integral , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[67]  Theodore Johnson,et al.  Fast Computation of 2-Dimensional Depth Contours , 1998, KDD.

[68]  Andreas Dengel,et al.  Histogram-based Outlier Score (HBOS): A fast Unsupervised Anomaly Detection Algorithm , 2012 .