DocumentCode :
2283033
Title :
Two-dimensional Sparse Principal Component Analysis: A new technique for feature extraction
Author :
Xiao, Cuntao ; Wang, Zhenyou
Volume :
2
fYear :
2010
fDate :
10-12 Aug. 2010
Firstpage :
976
Lastpage :
980
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.
Keywords :
covariance matrices; face recognition; feature extraction; principal component analysis; regression analysis; LDA algorithm; bi-directional two-dimensional sparse principal component analysis; elastic net regularization; face recognition; feature extraction; image covariance matrix; matrix-to-vector transformation; projection vector sparsity; ridge regression problem; Covariance matrix; Databases; Face; Face recognition; Principal component analysis; Strontium; Training; Two-dimensional sparse principal component analysis; elastic net; face recognition; feature extraction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
Type :
conf
DOI :
10.1109/ICNC.2010.5582886
Filename :
5582886
Link To Document :
بازگشت