Computer Science and Information Engineering, World Congress on (2009)

Los Angeles, California USA

Mar. 31, 2009 to Apr. 2, 2009

ISBN: 978-0-7695-3507-4

pp: 43-47

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.164

ABSTRACT

A new algorithm, Laplacian MinMax Discriminant Projection (LMMDP), is proposed in this paper for supervised dimensionality reduction. LMMDP aims at learning a linear transformation which is an extension of Linear Discriminant Analysis (LDA). Speciﬁcally, we deﬁne the within-class scatter and the between-class scatter using similarities which are based on pairwise distances in sample space. After the transformation, the considered pairwise samples within the same class are as close as possible, while those between classes are as far as possible. The structural information of classes is contained in the within-class and the between-class Laplacian matrices. Thus the discriminant projection subspace can be derived by controlling the structural evolution of Laplacian matrices. The performance on several data sets demonstrates the competence of the proposed algorithm.

INDEX TERMS

Linear Discriminant Analysis, Laplacian Matrix, Dimensionality Reduction, Supervised Learning

CITATION

J. Zhao and Z. Zheng, "Laplacian MinMax Discriminant Projections,"

*2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE)*, Los Angeles, CA, 2009, pp. 43-47.

doi:10.1109/CSIE.2009.164

CITATIONS