Kernel clustering-based discriminant analysis

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

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Detail(s)

Original languageEnglish
Pages (from-to)324-327
Journal / PublicationPattern Recognition
Volume40
Issue number1
Publication statusPublished - Jan 2007

Abstract

In this paper, a kernelized version of clustering-based discriminant analysis is proposed that we name KCDA. The main idea is to first map the original data into another high-dimensional space, and then to perform clustering-based discriminant analysis in the feature space. Kernel fuzzy c-means algorithm is used to do clustering for each class. A group of tests on two UCI standard benchmarks have been carried out that prove our proposed method is very promising. © 2006 Pattern Recognition Society.

Research Area(s)

  • Clustering-based discriminant analysis (CDA), Kernel clustering-based discriminant analysis (KCDA), Kernel fuzzy c-means, Kernel linear discriminant analysis (KLDA), Linear discriminant analysis (LDA)

Citation Format(s)

Kernel clustering-based discriminant analysis. / Ma, Bo; Qu, Hui-yang; Wong, Hau-san.
In: Pattern Recognition, Vol. 40, No. 1, 01.2007, p. 324-327.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review