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

Kwok Po NG*, Lizhi LIAO, Leihong Zhang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

16 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)223-235
Number of pages13
JournalNumerical Linear Algebra with Applications
Issue number2
Publication statusPublished - Mar 2011

Scopus Subject Areas

  • Algebra and Number Theory
  • Applied Mathematics

User-Defined Keywords

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


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