NDLI: Regularized max-min distance analysis for dimension reduction using the fisher criterion

Content Provider	IEEE Xplore Digital Library
Author	Guo-Wan Shao
Copyright Year	2012
Description	Author affiliation: Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074, China (Guo-Wan Shao)
Abstract	Under the homoscedastic Gaussian assumption, max-min distance analysis for dimension reduction has been developed to guarantee the separation of class pairs in the subspace. However, the method suffers from two problems. Firstly, close class pairs may still exist and tend to overlap in the subspace. As a result, a suboptimal classification error rate may be obtained. Secondly, local max-min distance analysis achieves better classification performance by applying an iteration technique, but it greatly increases the computations involved. In this paper, regularized max-min distance analysis for dimension reduction is proposed to deal with the above problems by introducing the Fisher criterion. In addition, a kernel trick is used to extend regularized max-min distance analysis to cope with the general case of data distribution. Experimental results on both synthetic data sets and real data sets show that the proposed algorithm can outperform or be comparable to local max-min distance analysis in almost all the cases without resorting to the iteration procedure.
Starting Page	124
Ending Page	129
File Size	1553590
Page Count	6
File Format	PDF
ISBN	9781467314848
ISSN	2160133X
e-ISBN	9781467314879
DOI	10.1109/ICMLC.2012.6358898
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Abstracts Face Nickel Regularization Dimension reduction Max-min distance analysis Fisher criterion
Content Type	Text
Resource Type	Article

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