Kernel clustering-based discriminant analysis

Bo Ma; Hui-yang Qu; Hau-san Wong

doi:10.1016/j.patcog.2006.05.033

Kernel clustering-based discriminant analysis

Bo Ma
, Hui-yang Qu
, Hau-san Wong

Department of Computer Science

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

41 Citations (Scopus)

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.

Original language	English
Pages (from-to)	324-327
Journal	Pattern Recognition
Volume	40
Issue number	1
DOIs	https://doi.org/10.1016/j.patcog.2006.05.033
Publication status	Published - Jan 2007

Research Keywords

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

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title = "Kernel clustering-based discriminant analysis",

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. {\textcopyright} 2006 Pattern Recognition Society.",

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

author = "Bo Ma and Hui-yang Qu and Hau-san Wong",

year = "2007",

month = jan,

doi = "10.1016/j.patcog.2006.05.033",

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T1 - Kernel clustering-based discriminant analysis

AU - Ma, Bo

AU - Qu, Hui-yang

AU - Wong, Hau-san

PY - 2007/1

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N2 - 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.

AB - 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.

KW - Clustering-based discriminant analysis (CDA)

KW - Kernel clustering-based discriminant analysis (KCDA)

KW - Kernel fuzzy c-means

KW - Kernel linear discriminant analysis (KLDA)

KW - Linear discriminant analysis (LDA)

UR - https://www.scopus.com/pages/publications/33749258406

UR - https://www.scopus.com/pages/publications/33749258406#tab=impact

U2 - 10.1016/j.patcog.2006.05.033

DO - 10.1016/j.patcog.2006.05.033

M3 - RGC 21 - Publication in refereed journal

SN - 0031-3203

VL - 40

SP - 324

EP - 327

JO - Pattern Recognition

JF - Pattern Recognition

IS - 1

ER -

Kernel clustering-based discriminant analysis

Abstract

Research Keywords

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