[1] Hu Changhui, Lu Xiaobo, Du Yijun, Chen Wujun, et al. Direct linear discriminant analysisbased on column pivoting QR decomposition and economic SVD [J]. Journal of Southeast University (English Edition), 2013, 29 (4): 395-399. [doi:10.3969/j.issn.1003-7985.2013.04.008]

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Direct linear discriminant analysisbased on column pivoting QR decomposition and economic SVD()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

29

Issue:

2013 4

Page:

395-399

Research Field:

Computer Science and Engineering

Publishing date:

2013-12-20

Info

Title:

Direct linear discriminant analysisbased on column pivoting QR decomposition and economic SVD

Author(s):

Hu Changhui;  Lu Xiaobo;  Du Yijun;  Chen Wujun

School of Automation, Southeast University, Nanjing 210096, China
Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education, Southeast University, Nanjing 210096, China

Keywords:

direct linear discriminant analysis;  column pivoting orthogonal triangular decomposition;  economic singular value decomposition;  dimension reduction;  feature extraction

PACS:

TP391

DOI:

10.3969/j.issn.1003-7985.2013.04.008

Abstract:

A direct linear discriminant analysis algorithm based on economic singular value decomposition(DLDA/ESVD)is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular(QR)decomposition and ESVD(DLDA/QR-ESVD)is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.

References:

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Memo

Memo:

Biographies: Hu Changhui(1983—), male, graduate; Lu Xiaobo(corresponding author), male, doctor, professor, xblu@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No.61374194).
Citation: Hu Changhui, Lu Xiaobo, Du Yijun, et al.Direct linear discriminant analysis based on column pivoting QR decomposition and economic SVD[J].Journal of Southeast University(English Edition), 2013, 29(4):395-399.[doi:10.3969/j.issn.1003-7985.2013.04.008]

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