http://dx.doi.org/10.1137/110851377">
 

Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Sparse orthogonal linear discriminant analysis

Language

English

Abstract

In this paper, sparse orthogonal linear discriminant analysis (OLDA) is studied. The main contributions of the present work include the following: (i) all minimum Frobeniusnorm/dimension solutions of the optimization problem used for establishing OLDA are characterized explicitly; and (ii) this explicit characterization leads to two numerical algorithms for computing a sparse linear transformation for OLDA. The first is based on the gradient flow approach while the second is a sequential linear Bregman method. We experiment with real world datasets to illustrate that the sequential linear Bregman method is much better than the gradient flow approach. The sequential linear Bregman method always achieves comparable classification accuracy with the normal OLDA, satisfactory sparsity and orthogonality, and acceptable CPU times. © 2012 Society for Industrial and Applied Mathematics.

Keywords

Dimensionality reduction, Linear discriminant analysis, Sparsity

Publication Date

2012

Source Publication Title

SIAM Journal on Scientific Computing

Volume

34

Issue

5

Start Page

A2421

End Page

A2443

Publisher

Society for Industrial and Applied Mathematics

ISSN (print)

10648275

ISSN (electronic)

10957197

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