Novelty detection: a review - part 1: statistical approaches

By Markos Markou, Sameer Singh. 2003

1,597 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Epidemiology

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Citations in the corpus

Towards Open World Recognition (2014, background): “Novelty detection and outlier detection are complex issues in their own right with long histories [26, 14] and they are still active vision research topics [2, 25].”
Anomaly Detection with Score functions based on Nearest Neighbor Graphs (2009, background): “It is also r efer ed to as novelty detection [1, 2], outlier detection [3], one-class classification [4, 5] and s ingle-class classification [6] in the literature.”
Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY (2020, methodology): “[12,13,21,30,31,38,142,148,149,171,192–195] Unsupervised/semi-Statistical/Novelty detection [58,65,86,89–92, 103, 115, 129, 196– 202] supervised classiﬁers Gaussian Artiﬁcial neural networks are commonly used classiﬁers due to them being universal function approximators [206].”
Kernel PCA for novelty detection (2007, background): “Novelty detection is one-class classiﬁcation—for a review see Markou and Singh [1,2].”
Toward Supervised Anomaly Detection (2014, background): “…describes the deviations of the new instance compared to the average data instance and, if the deviation exceeds a predeﬁned threshold, the instance is considered an anomaly or an outlier and processed adequately (Markou & Singh, 2003a; Chandola, Banerjee, & Kumar, 2009; Markou & Singh, 2003b).”
Feature bagging for outlier detection (2005, background): “The problem of detecting outliers (rare events) has been variously called in different research communities: novelty detection [23], chance discovery [24], outlier/anomaly detection [3, 5, 10, 19, 27, 36], exception mining [29], mining rare classes [11, 16-18], etc.”
ADBench: Anomaly Detection Benchmark (2022, background): “While ADBench focuses on the AD tasks, we note that there are some closely related problems, including out-of-distribution (OOD) detection [182, 183], novelty detection [116, 137], and open-set recognition (OSR) [51, 112].”
Local Novelty Detection in Multi-class Recognition Problems (2015, background): “Moreover, local novelty detection models are more suitable to manage the huge complexity of data distributions induced by features of real-world images.”
A Novel Anomaly Detection Scheme Based on Principal Component Classifier (2003, background): “An extensive review of a number of approaches to novelty detection was given in [19][20].”
A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques (2008, background): “Recently, Markou and Singh [86, 87] gave a detailed review of novelty detection approaches, using statistical and neural network-based approaches.”
Open problems in the security of learning (2008, methodology): “One method for detecting abnormal data is the general technique of outlier detection (refer to, for example, Markou and Singh for a survey of the field [17]).”
Smart Anomaly Detection in Sensor Systems (2020, methodology): “Time series analysis [40],[41],[42] Statistical Non-parametric [3],[14],[19],[20] Signal processing [43],[44] Parametric [3],[14],[19],[20]”
Video-Based Abnormal Human Behavior Recognition—A Review (2012, background): “For example, [47] and [48] present statistical approaches to novelty detection, while [49] and [50] did a comparative”