Outlier detection (OD) requires the observation of all samples and aims to detect those that deviate significantly from the majority distribution. Therefore, their approaches are usually transductive, rather than inductive.
[BMC-2014]
Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range.
Authors: Xiang Wan, Wenqian Wang, Jiming Liu, Tiejun Tong
Institution: Hong Kong Baptist University; Northwestern University
[SIGMOD-2000]
Lof: identifying density-based local outliers.
Authors: Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander
Institution: University of Munich; University of British Columbia
[PAKDD-2002]
Enhancing effectiveness of outlier detections for low density patterns.
Authors: Jian Tang, Zhixiang Chen, Ada Wai-chee Fu, David W. Cheung
Institution: Chinese University of Hong Kong; University of Texas; University of Hong Kong
[ACM-2009]
Loop: local outlier probabilities.
Authors: Hans-Peter Kriegel, Peer Kroger, Erich Schubert, Arthur Zimek
Institution: Ludwig-Maximilians University
[DMKD-2012]
Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection.
Authors: Erich Schubert, Arthur Zimek, Hans-Peter Kriegel
Institution: Ludwig-Maximilians-University; University of Alberta
[ACM-1981]
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography.
Authors: Martin A. Fischler, Robert C. Bolles
Institution: SRI International
[WIREs-2011]
Robust statistics for outlier de- tection.
Authors: Peter J. Rousseeuw, Mia Hubert
Institution: Katholieke University
[NeurIPS-2018]
Efficient anomaly detection via matrix sketching.
Authors: Vatsal Sharan, Parikshit Gopalan, Udi Wieder
Institution: Stanford University; VMware Research
The most basic OD method model the entire dataset with the Gaussian distribution, and flag the samples over three standard deviations from the mean.
[KDD-1996]
A density-based algorithm for discovering clusters in large spatial databases with noise.
Authors: Martin Ester, Hans-Peter Kriegel, Jiirg Sander, Xiaowei Xu
Institution: University of Munich
[ECML-2007]
Class noise mitigation through instance weighting.
Authors: Umaa Rebbapragada, Carla E. Brodley
Institution: Tufts University
Similar to "three standard deviations" rules under the assumption that the data follows normal distribution, interquartile range can also be used to identify outliers.
[DMKD-2014]
Graph based anomaly detection and description: a survey.
Authors: Leman Akoglu; Hanghang Tong; Danai Koutra
Institution: Stony Brook University, City University of New York, Carnegie Mellon University
[SIGKDD-2003]
Graph-based anomaly detection.
Authors: Caleb C. Noble, Diane J. Cook
Institution: University of Texas
[ICTAI-2007]
Spatial outlier detection: a graph-based approach.
Authors: Yufeng Kou, Chang-Tien Lu, Raimundo F. Dos Santos
Institution: Virginia Polytechnic Institute and State University
[ICCSE-2012]
A graph-based clustering algorithm for anomaly intrusion detection.
Authors: Zhou Mingqiang, Huang Hui, Wang Qian
Institution: Chongqing University
[ACM-2020]
Webly supervised image classification with metadata: Automatic noisy label correction via visual-semantic graph.
Authors: Jingkang Yang, Weirong Chen, Litong Feng, Xiaopeng Yan, Huabin Zheng, Wayne Zhang
Institution: Sensetime Research; Rice University; The Chinese University of Hong Kong; Shanghai Jiao Tong University
[-2002]
One-class classification: Concept learning in the absence of counter-examples.
Authors: Tax D.M.J
Institution: Technische Universiteit Delft
[ICMI-2018]
Deep one-class classification.
Authors: Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Muller, Marius Kloft
Institution: Humboldt University; Hasso Plattner Institute; TU Kaiserslautern; TU Berlin; University of Edinburgh; DFKI GmbH; Singapore University of Technology and Design
[ICDM-2008]
Isolation forest.
Authors: Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou
Institution: Monash University; Nanjing University
[CVPR-2017]
Learning from noisy labels with distillation.
Authors: Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, Li-Jia Li
Institution: Snap Inc.; Yahoo Research
[ICLR-2020]
Self: Learning to filter noisy labels with self-ensembling.
Authors: Duc Tam Nguyen, Chaithanya Kumar Mummadi, Thi Phuong Nhung Ngo, Thi Hoai Phuong Nguyen, Laura Beggel, Thomas Brox
Institution: University of Freiburg; Bosch Research; Bosch Center for AI; Karlsruhe Institute of Technology
[NIPS-2018]
Co-teaching: Robust training of deep neural networks with extremely noisy labels.
Authors: Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama
Institution: University of Technology Sydney; RIKEN; 4Paradigm Inc.; Stanford University; University of Tokyo
[ECCV-2020]
Webly supervised image classification with self- contained confidence.
Authors: Jingkang Yang, Litong Feng, Weirong Chen, Xiaopeng Yan, Huabin Zheng, Ping Luo, Wayne Zhang
Institution: SenseTime Research; Rice University; The Chinese University of Hong Kong; The University of Hong Kong