Comprehensive Analysis for the Local Fisher Discriminant Analysis

Junbin Gao, Paul Kwan, Xiaodi Huang

Research output: Contribution to journalArticle

4 Citations (Scopus)
13 Downloads (Pure)

Abstract

Using data local information, the recently proposed local Fisher Discriminant Analysis (LFDA) algorithm (Sugiyama, 2007) provides a new way of handling the multimodal issues within classes where the conventional Fisher Discriminant Analysis(FDA) algorithm fails. Like the FDA algorithm ' its global counterpart FDA algorithm, the LFDA suffers when it is applied to the higher dimensional data sets.In this paper we propose a new formulation by which a robust algorithm can beformed. The new algorithm offers more robust results for higher dimensional datasets when compared with the LFDA in most cases. By extensive simulation studies,we have demonstrated the practical usefulness and robustness of our new algorithmin data visualization.
Original languageEnglish
Pages (from-to)1129-1143
Number of pages15
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume23
Issue number6
DOIs
Publication statusPublished - Sep 2009

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