Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

On sparse linear discriminant analysis algorithm for high-dimensional data classification

Language

English

Abstract

In this paper, we present a sparse linear discriminant analysis (LDA) algorithm for high-dimensional objects in subspaces. In high dimensional data, groups of objects often exist in subspaces rather than in the entire space. For example, in text data classification, groups of documents of different types are categorized by different subsets of terms. The terms for one group may not occur in the samples of other groups. In the new algorithm, we consider a LDA to calculate a weight for each dimension and use the weight values to identify the subsets of important dimensions in the discriminant vectors that categorize different groups. This is achieved by including the weight sparsity term in the objective function that is minimized in the LDA. We develop an iterative algorithm for computing such sparse and orthogonal vectors in the LDA. Experiments on real data sets have shown that the new algorithm can generate better classification results and identify relevant dimensions. © 2010 John Wiley & Sons, Ltd..

Keywords

High-dimensional data, Linear discriminant analysis, Sparsity, Weighting

Publication Date

2011

Source Publication Title

Numerical Linear Algebra with Applications

Volume

18

Issue

2

Start Page

223

End Page

235

Publisher

Wiley

DOI

10.1002/nla.736

Link to Publisher's Edition

http://dx.doi.org/10.1002/nla.736

ISSN (print)

10705325

ISSN (electronic)

10991506

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