Discriminant analysis: A unified approach

Peng Zhang, Jing Peng, Norbert Riedel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations


Linear discriminant analysis (LDA) as a dimension reduction method is widely used in data mining and machine learning. It however suffers from the small sample size (SSS) problem when data dimensionality is greater than the sample size. Many modified methods have been proposed to address some aspect of this difficulty from a particular viewpoint. A. comprehensive framework that provides a complete solution to the SSS problem is still missing. In this paper, we provide a unified approach to LDA, and investigate the SSS problem in the framework of statistical learning theory. In such a unified approach, our analysis results in a deeper understanding of LDA. We demonstrate that LDA (and its nonlinear extension) belongs to the same framework -where powerful classifiers such as support vector machines (SVMs) are formulated. In addition, this approach allows us to establish an error bound for LDA. Finally our experiments validate our theoretical analysis results.

Original languageEnglish
Title of host publicationProceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
Number of pages8
StatePublished - 2005
Event5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States
Duration: 27 Nov 200530 Nov 2005

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other5th IEEE International Conference on Data Mining, ICDM 2005
Country/TerritoryUnited States
CityHouston, TX


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