Incremental principal component analysis using the approximated covariance matrix

被引：0

作者：

Cao X.-H. ^{[1
]}

Liu H.-W. ^{[2
]}

Wu S.-J. ^{[2
]}

机构：

[1] Research Inst. of Electronic Countermeasures, Xidian Univ

[2] Key Lab. of Radar Signal Processing, Xidian Univ

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2010年 / 37卷 / 03期

关键词：

Approximated covariance matrix; Eigenvalue decomposition; Incremental principal component analysis; Subspace projection;

D O I：

10.3969/j.issn.1001-2400.2010.03.013

中图分类号：

学科分类号：

摘要：

Firstly, with eigenvectors orthogonal to each other, the computation complexity of the subspace projection(SP) algorithm is reduced to 1/P of the original algorithm(where P is the number of desired eigencomponents). Then, the covariance matrix is replaced by the approximated covariance matrix which is composed of large eigenvalues and corresponding eigenvectors, the computation complexity can be reduced to 1/N of the original algorithm(where N is the input vector dimension)further. Finally, experimental results based on the ORL face database demonstrate the efficiency of the presented algorithm.

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页码：459 / 463

页数：4