Two-dimensional sparse principal component analysis for palmprint recognition

Authors:
Cuntao Xiao
Affiliations:
Faculty of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China and Faculty of Applied Mathematics, Guangdong University of Technology, Guangzhou, China
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

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Abstract

Principal Component Analysis(PCA) is intrinsically a ridge regression problem in statistical view. By imposing l1 constraint on the regression coefficients, Sparse Principal Component Analysis(SPCA) which is easier to interpret and better for generalization is obtained. But traditional SPCA is difficult to be used on 2-d 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, Two-dimensional Sparse Principal Component Analysis(2dSPCA) is proposed to overcome the above shortcoming of SPCA. 2dSPCA is directly calculated by elastic net regularization on image covariancematrix without vectorization. Sparsity of projection vectors makes the results more interpretable and generalizable. Experiment on PolyU palmprint databases shows that 2dSPCA achieves comparable or higher performance compared with 2dPCA.