NDLI: Two-dimensional Sparse Principal Component Analysis: A new technique for feature extraction

Content Provider	IEEE Xplore Digital Library
Author	Cuntao Xiao Zhenyou Wang
Copyright Year	2010
Description	Author affiliation: Faculty of Applied Mathematics, Guangdong University of Technology, Guangzhou, China (Zhenyou Wang) \|\| Faculty of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China (Cuntao Xiao)
Abstract	Principal Component Analysis(PCA) is intrinsically a ridge regression problem in statistical view. By imposing l1 constraint on the regression coefficients, we have Sparse Principal Component Analysis(SPCA) which is easier to interpret and better for generalization. But traditional SPCA is difficult to be used on 2-d face data for its high dimensionality of covariance matrix because of the matrix-to-vector transformation, especially when the number of dimensionality and training samples are all in large scale. In this paper,we proposed a bi-directional Two-dimensional Sparse Principal Component Analysis(2dSPCA) to overcome the above shortcoming of SPCA. 2dSPCA is directly calculated by elastic net regularization on image covariance matrix without vectorization. Sparsity of projection vectors makes the results more interpretable,also helps us find the important local areas of face image for face recognition,for example, the areas around the corner of eye,nose and mouth include significantly discriminative information. Experiments on some benchmark face databases show that 2dSPCA achieves comparable or higher performance in face recognition compared with 2dSPCA. We also propose a 2dSPCA+LDA algorithm to improve the effectiveness of face recognition.
Starting Page	976
Ending Page	980
File Size	146424
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5582886
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Strontium Databases Face recognition feature extraction face recognition Covariance matrix Face elastic net Two-dimensional sparse principal component analysis Principal component analysis
Content Type	Text
Resource Type	Article

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