NDLI: Efficient and robust feature extraction by maximum margin criterion

Content Provider	IEEE Xplore Digital Library
Author	Haifeng Li Tao Jiang Keshu Zhang
Copyright Year	1990
Abstract	In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the two most popular linear dimensionality reduction methods. However, PCA is not very effective for the extraction of the most discriminant features, and LDA is not stable due to the small sample size problem . In this paper, we propose some new (linear and nonlinear) feature extractors based on maximum margin criterion (MMC). Geometrically, feature extractors based on MMC maximize the (average) margin between classes after dimensionality reduction. It is shown that MMC can represent class separability better than PCA. As a connection to LDA, we may also derive LDA from MMC by incorporating some constraints. By using some other constraints, we establish a new linear feature extractor that does not suffer from the small sample size problem, which is known to cause serious stability problems for LDA. The kernelized (nonlinear) counterpart of this linear feature extractor is also established in the paper. Our extensive experiments demonstrate that the new feature extractors are effective, stable, and efficient.
Sponsorship	IEEE Computational Intelligence Society
Page Count	9
File Size	591959
Starting Page	157
Ending Page	165
File Format	PDF
ISSN	10459227
Volume Number	17
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Feature extraction Linear discriminant analysis Principal component analysis Pattern recognition Vectors Covariance matrix Data mining Stability Degradation small sample size problem linear discriminant analysis (LDA) maximum margin criterion (MMC)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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