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A Core Set Based Large Vector-Angular Region and Margin Approach for Novelty Detection
Joint Authors
Chen, Jiusheng
Zhang, Xiaoyu
Guo, Kai
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-02-10
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
A large vector-angular region and margin (LARM) approach is presented for novelty detection based on imbalanced data.
The key idea is to construct the largest vector-angular region in the feature space to separate normal training patterns; meanwhile, maximize the vector-angular margin between the surface of this optimal vector-angular region and abnormal training patterns.
In order to improve the generalization performance of LARM, the vector-angular distribution is optimized by maximizing the vector-angular mean and minimizing the vector-angular variance, which separates the normal and abnormal examples well.
However, the inherent computation of quadratic programming (QP) solver takes O(n3) training time and at least O(n2) space, which might be computational prohibitive for large scale problems.
By (1+ε) and (1-ε)-approximation algorithm, the core set based LARM algorithm is proposed for fast training LARM problem.
Experimental results based on imbalanced datasets have validated the favorable efficiency of the proposed approach in novelty detection.
American Psychological Association (APA)
Chen, Jiusheng& Zhang, Xiaoyu& Guo, Kai. 2016. A Core Set Based Large Vector-Angular Region and Margin Approach for Novelty Detection. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1111769
Modern Language Association (MLA)
Chen, Jiusheng…[et al.]. A Core Set Based Large Vector-Angular Region and Margin Approach for Novelty Detection. Mathematical Problems in Engineering No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1111769
American Medical Association (AMA)
Chen, Jiusheng& Zhang, Xiaoyu& Guo, Kai. A Core Set Based Large Vector-Angular Region and Margin Approach for Novelty Detection. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1111769
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1111769