General sparse multi-class linear discriminant analysis

Sandra E. Safo, Jeongyoun Ahn

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Discrimination with high dimensional data is often more effectively done with sparse methods that use a fraction of predictors rather than using all the available ones. In recent years, some effective sparse discrimination methods based on Fisher's linear discriminant analysis (LDA) have been proposed for binary class problems. Extensions to multi-class problems are suggested in those works; however, they have some drawbacks such as the heavy computational cost for a large number of classes. We propose an approach to generalize a binary LDA solution into a multi-class solution while avoiding the limitations of the existing methods. Simulation studies with various settings, as well as real data examples including next generation sequencing data, confirm the effectiveness of the proposed approach.

Original languageEnglish (US)
Pages (from-to)81-90
Number of pages10
JournalComputational Statistics and Data Analysis
StatePublished - Jul 2016

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V. All rights reserved.


  • Classification
  • Linear discriminant analysis
  • Multi-class discrimination
  • Singular value decomposition
  • Sparse discrimination


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