Essence of kernel Fisher discriminant: KPCA plus LDA

Jian Yang; Zhong Jin; Jing Yu Yang; Dapeng Zhang; Alejandro F. Frangi

doi:10.1016/j.patcog.2003.10.015

Essence of kernel Fisher discriminant: KPCA plus LDA

Jian Yang, Zhong Jin, Jing Yu Yang, Dapeng Zhang, Alejandro F. Frangi

Research output: Journal article publication › Journal article › Academic research › peer-review

175 Citations (Scopus)

Abstract

In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). Based on this result, a more transparent KFD algorithm is proposed. That is, KPCA is first performed and then LDA is used for a second feature extraction in the KPCA-transformed space. Finally, the effectiveness of the proposed algorithm is verified using the CENPARMI handwritten numeral database.

Original language	English
Pages (from-to)	2097-2100
Number of pages	4
Journal	Pattern Recognition
Volume	37
Issue number	10
DOIs	https://doi.org/10.1016/j.patcog.2003.10.015
Publication status	Published - 1 Oct 2004

Keywords

Feature extraction
Fisher linear discriminant analysis
Handwritten numeral recognition
Kernel-based methods
Principal component analysis

ASJC Scopus subject areas

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/j.patcog.2003.10.015

Cite this

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title = "Essence of kernel Fisher discriminant: KPCA plus LDA",

abstract = "In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). Based on this result, a more transparent KFD algorithm is proposed. That is, KPCA is first performed and then LDA is used for a second feature extraction in the KPCA-transformed space. Finally, the effectiveness of the proposed algorithm is verified using the CENPARMI handwritten numeral database.",

keywords = "Feature extraction, Fisher linear discriminant analysis, Handwritten numeral recognition, Kernel-based methods, Principal component analysis",

author = "Jian Yang and Zhong Jin and Yang, {Jing Yu} and Dapeng Zhang and Frangi, {Alejandro F.}",

year = "2004",

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T1 - Essence of kernel Fisher discriminant: KPCA plus LDA

AU - Yang, Jian

AU - Jin, Zhong

AU - Yang, Jing Yu

AU - Zhang, Dapeng

AU - Frangi, Alejandro F.

PY - 2004/10/1

Y1 - 2004/10/1

N2 - In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). Based on this result, a more transparent KFD algorithm is proposed. That is, KPCA is first performed and then LDA is used for a second feature extraction in the KPCA-transformed space. Finally, the effectiveness of the proposed algorithm is verified using the CENPARMI handwritten numeral database.

AB - In this paper, the method of kernel Fisher discriminant (KFD) is analyzed and its nature is revealed, i.e., KFD is equivalent to kernel principal component analysis (KPCA) plus Fisher linear discriminant analysis (LDA). Based on this result, a more transparent KFD algorithm is proposed. That is, KPCA is first performed and then LDA is used for a second feature extraction in the KPCA-transformed space. Finally, the effectiveness of the proposed algorithm is verified using the CENPARMI handwritten numeral database.

KW - Feature extraction

KW - Fisher linear discriminant analysis

KW - Handwritten numeral recognition

KW - Kernel-based methods

KW - Principal component analysis

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DO - 10.1016/j.patcog.2003.10.015

M3 - Journal article

SN - 0031-3203

VL - 37

SP - 2097

EP - 2100

JO - Pattern Recognition

JF - Pattern Recognition

IS - 10

ER -

Essence of kernel Fisher discriminant: KPCA plus LDA

Abstract

Keywords

ASJC Scopus subject areas

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