NDLI: Locally Supervised Discriminant Analysis in Kernel Space

Content Provider	IEEE Xplore Digital Library
Author	Caikou Chen Jun Shi Yiming Yu
Copyright Year	2009
Abstract	Considering inherent limitations of such locality-based dimensionality reduction methods as unsupervised discriminant projection (UDP), a novel manifold-base feature extraction method, called locally supervised discriminant analysis in kernel space, is proposed in the paper. It is a locally nonlinear and supervised dimensionality reduction method, which takes into account the locality, kernel mapping and class information simultaneously in the process of feature extraction. The proposed algorithm seeks to find a projection that maximizes the kernel non-local scatter, while minimizes the kernel local scatter and the kernel within-class scatter. As a result, the final projection vectors could have more powerful discriminant abilities since it not only captures the intrinsic nonlinear change of data, but also preserve the faithful locality. The experimental results on Yale face database show that the proposed method outperforms the LDA and UDP.
Starting Page	358
Ending Page	362
File Size	301567
Page Count	5
File Format	PDF
ISBN	9781424454112
e-ISBN	9781424454129
DOI	10.1109/CIS.2009.153
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	kernel space Face recognition feature extraction Scattering Information security Feature extraction Educational institutions Linear discriminant analysis Kernel Computational intelligence Principal component analysis Information analysis
Content Type	Text
Resource Type	Article

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