Producing computationally efficient KPCA-based feature extraction for classification problems

Y. Xu; C. Lin; W. Zhao

doi:10.1049/el.2010.2814

Producing computationally efficient KPCA-based feature extraction for classification problems

Y. Xu^*
, C. Lin
, W. Zhao

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen
Wuhan University of Technology

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

An improvement to kernel principal component analysis (KPCA) to produce computationally efficient KPCA-based feature extraction is proposed. This improvement is applicable to all cases no matter whether the samples in the feature space have zero mean or not. Experiments on several benchmark datasets show that the improvement performs well in classification problems.

Original language	English
Pages (from-to)	452-453
Number of pages	2
Journal	Electronics Letters
Volume	46
Issue number	6
DOIs	https://doi.org/10.1049/el.2010.2814
State	Published - 2010
Externally published	Yes

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10.1049/el.2010.2814

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title = "Producing computationally efficient KPCA-based feature extraction for classification problems",

abstract = "An improvement to kernel principal component analysis (KPCA) to produce computationally efficient KPCA-based feature extraction is proposed. This improvement is applicable to all cases no matter whether the samples in the feature space have zero mean or not. Experiments on several benchmark datasets show that the improvement performs well in classification problems.",

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AB - An improvement to kernel principal component analysis (KPCA) to produce computationally efficient KPCA-based feature extraction is proposed. This improvement is applicable to all cases no matter whether the samples in the feature space have zero mean or not. Experiments on several benchmark datasets show that the improvement performs well in classification problems.

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U2 - 10.1049/el.2010.2814

DO - 10.1049/el.2010.2814

M3 - 文章

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SN - 0013-5194

VL - 46

SP - 452

EP - 453

JO - Electronics Letters

JF - Electronics Letters

IS - 6

ER -

Producing computationally efficient KPCA-based feature extraction for classification problems

Abstract

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