Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11381
Title: Complete large margin linear discriminant analysis using mathematical programming approach
Authors: Chen, X
Yang, J
Zhang, D 
Liang, J
Keywords: Constrained concave-convex procedure
Feature extraction
Large margin classifier
Linear discriminant analysis
Issue Date: 2013
Source: Pattern recognition, 2013, v. 46, no. 6, p. 1579-1594 How to cite?
Journal: Pattern Recognition 
Abstract: In this paper, we develop a novel dimensionality reduction (DR) framework coined complete large margin linear discriminant analysis (CLMLDA). Inspired by several recently proposed DR methods, CLMLDA constructs two mathematical programming models by maximizing the minimum distance between each class center and the total class center respectively in the null space of within-class scatter matrix and its orthogonal complementary space. In this way, CLMLDA not only makes full use of the discriminative information contained in the whole feature space but also overcome the weakness of linear discriminant analysis (LDA) in dealing with the class separation problem. The solutions of CLMLDA follow from solving two nonconvex optimization problems, each of which is transformed to a series of convex quadratic programming problems by using the constrained concave-convex procedure first, and then solved by off-the-shelf optimization toolbox. Experiments on both toy and several publicly available databases demonstrate its feasibility and effectiveness.
URI: http://hdl.handle.net/10397/11381
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2012.11.019
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