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Generalized mean for feature extraction in one-class classification problems

Cited 7 time in Web of Science Cited 8 time in Scopus
Authors
Oh, Jiyong; Kwak, Nojun; Lee, Minsik; Choi, Chong-Ho
Issue Date
2013-12
Publisher
Elsevier
Citation
Pattern Recognition, Vol.46 No.12, pp. 3328-3340
Keywords
공학Generalized meanBiased discriminant analysisFeature extractionDimensionality reductionOne-class classification
Abstract
Biased discriminant analysis (BDA), which extracts discriminative features for one-class classification problems, is sensitive to outliers in negative samples. This study focuses on the drawback of BDA attributed to the objective function based on the arithmetic mean in one-class classification problems, and proposes an objective function based on a generalized mean. A novel method is also presented to effectively maximize the objective function. The experimental results show that the proposed method provides better discriminative features than the BDA and its variants. (C) 2013 Elsevier Ltd. All rights reserved.
ISSN
0031-3203
Language
English
URI
http://hdl.handle.net/10371/91636
DOI
https://doi.org/10.1016/j.patcog.2013.06.018
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Graduate School of Convergence Science and Technology (융합과학기술대학원)Dept. of Transdisciplinary Studies(융합과학부)Journal Papers (저널논문_융합과학부)
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