Outlier Detection

Outlier Detection is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.

Source: Coverage-based Outlier Explanation

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 691–700 of 703 papers

Title	Date	Tasks	Status
Noisy Subspace Clustering via Thresholding	May 15, 2013	ClusteringOutlier Detection	—Unverified
A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization	Apr 10, 2013	ClusteringMotion Segmentation	—Unverified
Robust PCA and subspace tracking from incomplete observations using L0-surrogates	Oct 2, 2012	Outlier Detection	—Unverified
Irregularity Detection in Categorized Document Corpora	May 1, 2012	ArticlesDocument Classification	—Unverified
Detection of Peculiar Word Sense by Distance Metric Learning with Labeled Examples	May 1, 2012	General ClassificationMetric Learning	—Unverified
Shaping Level Sets with Submodular Functions	Dec 1, 2011	Change Point DetectionClustering	—Unverified
Relative Density-Ratio Estimation for Robust Distribution Comparison	Dec 1, 2011	Density Ratio EstimationOutlier Detection	—Unverified
Outlier detection in default logics: the tractability/intractability frontier	Sep 21, 2011	LEMMAOutlier Detection	—Unverified
Exact Subspace Segmentation and Outlier Detection by Low-Rank Representation	Sep 8, 2011	Outlier Detection	—Unverified
Condition Number Analysis of Kernel-based Density Ratio Estimation	Dec 15, 2009	Density Ratio Estimationfeature selection	CodeCode Available

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All datasets ECG5000 Balance scale_class 1 Breast cancer Wisconsin_class 2 Breast cancer Wisconsin_class 4 Fashion-MNIST Glass identification Heart-C Hepatitis Internet Ad Ionosphere_class b SKAB

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	VRAE+SVM	Accuracy	0.98	—	Unverified
2	F-t ALSTM-FCN	Accuracy	0.95	—	Unverified
3	GENDIS	Accuracy	0.94	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	99.03	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	37.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	65.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PAE	AUROC	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	99.05	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC	0.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC-ROC	0.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MIX	AUC-ROC	0.93	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ASVDD	Average Accuracy	86.33	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LSTMCaps	Average F1	0.74	—	Unverified