[1]DENG Mao,CHEN Xu,CHEN Tian-xiang,et al.mproved kernel principal component analysis based ona clustering algorithm[J].CAAI Transactions on Intelligent Systems,2010,5(3):221-226.
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mproved kernel principal component analysis based ona clustering algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 3 Page number: 221-226 Column: 学术论文—机器学习 Public date: 2010-06-25
Title:
mproved kernel principal component analysis based ona clustering algorithm
Author(s):
DENG Mao1;  CHEN Xu1;  CHEN Tian-xiang2;  WANG Hui-rong1;  LU Hua-xiang1
1.Institute of Semiconductors, Chinese Academy Sciences, Beijing 100083, China;
2.Department of Electronic and Electrical Engineering, Xiamen University of Technology, Xiamen 361005, China
Keywords:
KPCA;  kernel clustering;  partition of training data set;  covariance matrix;  eigenvector
CLC:
TP18
DOI:
-
Abstract:
To overcome the computational problems of the standard kernel principal component analysis (KPCA) algorithm, the authors proposed a new method for eigenvector selection by evaluating the cumulative contribution rate of the eigenvalues of the covariance matrix. In addition, a new way to partition the training data set based on kernel clustering was also developed. The influence was then explored of different partitions of training data sets on the size of the final kernel matrix, on the conditions causing a given cumulative contribution rate, and on the number of subsets. Experimental results showed that a smaller kernel matrix can be obtained when kernel clustering method are used to partition the training dataset. The proposed algorithm can be helpful to reduce the time complexity of the eigen decomposition of a kernel matrix and to improve the speed of feature extraction for test samples.
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