Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Novelty detection using level set methods

Ding, Xuemei, Li, Yuhua, Belatreche, Ammar and Maguire, Liam P. 2015. Novelty detection using level set methods. IEEE Transactions on Neural Networks and Learning Systems 26 (3) , pp. 576-588. 10.1109/TNNLS.2014.2320293

Full text not available from this repository.

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: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Data Innovation Research Institute (DIURI)
Publisher: IEEE
ISSN: 2162-237X
Last Modified: 04 Feb 2021 17:30
URI: https://orca.cardiff.ac.uk/id/eprint/109843

Citation Data

Cited 18 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item