SOTAVerified

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

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Showing 601–650 of 703 papers

TitleStatusHype
Scalable Support Vector Clustering Using Budget—0
LSTM Fully Convolutional Networks for Time Series ClassificationCode1
Zipporah: a Fast and Scalable Data Cleaning System for Noisy Web-Crawled Parallel Corpora—0
Identifying Semantically Deviating Outlier Documents—0
Fast Incremental SVDD Learning Algorithm with the Gaussian KernelCode0
Visualizing and Exploring Dynamic High-Dimensional Datasets with LION-tSNE—0
Detection of Abnormal Input-Output Associations—0
Anomaly Detection by Robust Statistics—0
KNN Ensembles for Tweedie Regression: The Power of Multiscale Neighborhoods—0
Exploring Outliers in Crowdsourced Ranking for QoE—0
Learning Geometric Concepts with Nasty Noise—0
Outlier-Robust Tensor PCA—0
Pay Attention to the Ending:Strong Neural Baselines for the ROC Story Cloze Task—0
ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction—0
A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric—0
The Geometry of Nodal Sets and Outlier Detection—0
Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization—0
REMIX: Automated Exploration for Interactive Outlier Detection—0
Provable Self-Representation Based Outlier Detection in a Union of Subspaces—0
Word Embeddings via Tensor FactorizationCode0
Efficient variational Bayesian neural network ensembles for outlier detectionCode0
Deep SetsCode1
Outlier Cluster Formation in Spectral Clustering—0
Interpreting Outliers: Localized Logistic Regression for Density Ratio Estimation—0
Outlier Detection for Text Data : An Extended VersionCode0
Meta-Unsupervised-Learning: A supervised approach to unsupervised learning—0
Detecting Unusual Input-Output Associations in Multivariate Conditional Data—0
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy—0
Cognitive Deep Machine Can Train Itself—0
Robust k-means: a Theoretical Revisit—0
Deep Learning with Sets and Point Clouds—0
Automated Generation of Multilingual Clusters for the Evaluation of Distributed RepresentationsCode0
Local Subspace-Based Outlier Detection using Global NeighbourhoodsCode0
Kernel Bandwidth Selection for SVDD: Peak Criterion Approach for Large Data—0
A Bayesian Ensemble for Unsupervised Anomaly Detection—0
A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques—0
Structured Group Sparsity: A Novel Indoor WLAN Localization, Outlier Detection, and Radio Map Interpolation Scheme—0
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting—0
Outlier Detection from Network Data with Subnetwork Interpretation—0
Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective—0
Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes—0
Multi-Person Pose Estimation with Local Joint-to-Person AssociationsCode0
Network Volume Anomaly Detection and Identification in Large-scale Networks based on Online Time-structured Traffic Tensor Tracking—0
Outlier Detection on Mixed-Type Data: An Energy-based ApproachCode0
Find the word that does not belong: A Framework for an Intrinsic Evaluation of Word Vector Representations—0
Robust Contextual Outlier Detection: Where Context Meets Sparsity—0
A Non-Parametric Control Chart For High Frequency Multivariate Data—0
Dealing with Class Imbalance using Thresholding—0
LSTM-based Encoder-Decoder for Multi-sensor Anomaly DetectionCode2
A Local Density-Based Approach for Local Outlier Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98—Unverified
2F-t ALSTM-FCNAccuracy0.95—Unverified
3GENDISAccuracy0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6—Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74—Unverified