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Novelty Detection Using Level Set Methods

Ding, Xuemei, Li, Yuhua, Belatreche, Ammar and Maguire, Liam (2015) Novelty Detection Using Level Set Methods. IEEE Transactions on Neural Networks and Learning Systems, 26 (3). pp. 576-588. [Journal article]

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URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6817597

DOI: 10.1109/TNNLS.2014.2320293

Abstract

This paper presents a level set boundary description (LSBD) approach for novelty detection that treats the nonlinear boundary directly in the input space. The proposed approach consists of level set function (LSF) construction, boundary evolution, and termination of the training process. It employs kernel density estimation to construct the LSF of the initial boundary for the training data set. Then, a sign of the LSF-based algorithm is proposed to evolve the boundary and make it fit more tightly in the data distribution. The training process terminates when an expected fraction of rejected normal data is reached. The evolution process utilizes the signs of the LSF values at all training data points to decide whether to expand or shrink the boundary. Extensive experiments are conducted on benchmark data sets to evaluate the proposed LSBD method and compare it against four representative novelty detection methods. The experimental results demonstrate that the novelty detector modeled with the proposed LSBD can effectively detect anomalies.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:31093
Deposited By: Dr Ammar Belatreche
Deposited On:19 Mar 2015 16:47
Last Modified:19 Mar 2015 16:47

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