article

Review: A review of novelty detection

Authors:

Marco A. F. Pimentel,

David A. Clifton,

Lionel TarassenkoAuthors Info & Claims

Signal Processing, Volume 99

Pages 215 - 249

https://doi.org/10.1016/j.sigpro.2013.12.026

Published: 01 June 2014 Publication History

Abstract

Novelty detection is the task of classifying test data that differ in some respect from the data that are available during training. This may be seen as ''one-class classification'', in which a model is constructed to describe ''normal'' training data. The novelty detection approach is typically used when the quantity of available ''abnormal'' data is insufficient to construct explicit models for non-normal classes. Application includes inference in datasets from critical systems, where the quantity of available normal data is very large, such that ''normality'' may be accurately modelled. In this review we aim to provide an updated and structured investigation of novelty detection research papers that have appeared in the machine learning literature during the last decade.

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Review: A review of novelty detection
1. Computing methodologies
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cover image Signal Processing

Signal Processing Volume 99, Issue

June, 2014

250 pages

ISSN:0165-1684

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Copyright © © 2014.

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Elsevier North-Holland, Inc.

United States

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Published: 01 June 2014

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Wu ADeng C(2024)TIB: Detecting Unknown Objects via Two-Stream Information BottleneckIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.332352346:1(611-625)Online publication date: 1-Jan-2024
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Wu ADeng CLiu W(2024)Unsupervised Out-of-Distribution Object Detection via PCA-Driven Dynamic Prototype EnhancementIEEE Transactions on Image Processing10.1109/TIP.2024.337846433(2431-2446)Online publication date: 22-Mar-2024
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