Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34276
Title: Robust kernel discriminant analysis and its application to feature extraction and recognition
Authors: Liang, Z
Zhang, D 
Shi, P
Keywords: Character recognition
Dimensionality reduction
Face recognition
Kernel trick
Robust kernel discriminant analysis
Subspace analysis
Issue Date: 2006
Publisher: Elsevier
Source: Neurocomputing, 2006, v. 69, no. 7-9 SPEC. ISS., p. 928-933 How to cite?
Journal: Neurocomputing 
Abstract: Subspace analysis is an effective technique for dimensionality reduction, which aims at finding a low-dimensional space of high-dimensional data. In this paper, a novel subspace method called robust kernel discriminant analysis is proposed for dimensionality reduction. An optimization function is firstly defined in terms of the distance between similar elements and the distance between dissimilar elements, which can preserve the structure of the data in the mapping space. Then the optimization function is transformed into an eigenvalue problem and the projection vectors are obtained by solving the eigenvalue problem. Finally, experimental results on face images and handwritten numerical characters demonstrate the effectiveness and feasibility of the proposed method.
URI: http://hdl.handle.net/10397/34276
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2005.09.001
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