Sparse orthogonal linear discriminant analysis

Delin Chut*, Lizhi LIAO, Kwok Po NG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)A2421-A2443
JournalSIAM Journal of Scientific Computing
Volume34
Issue number5
DOIs
Publication statusPublished - 2012

Scopus Subject Areas

  • Computational Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Dimensionality reduction
  • Linear discriminant analysis
  • Sparsity

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