A unified view of two-dimensional principal component analyses

Authors:
Kohei Inoue;Kenji Hara;Kiichi Urahama
Affiliations:
Department of Communication Design Science, Kyushu University, Minami-ku, Fukuoka, Japan;Department of Communication Design Science, Kyushu University, Minami-ku, Fukuoka, Japan;Department of Communication Design Science, Kyushu University, Minami-ku, Fukuoka, Japan
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

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Abstract

Recently, two-dimensional principal component analysis (2D-PCA) and its variants have been proposed by several researchers. In this paper, we summarize their 2DPCA variants, show some equivalence among them, and present a unified view in which the non-iterative 2DPCA variants are interpreted as the non-iterative approximate algorithms for the iterative 2DPCA variants, i.e., the non-iterative 2DPCA variants are derived as the first iterations of the iterative algorithm started from different initial settings. Then we classify the non-iterative 2DPCA variants on the basis of their algorithmic patterns and propose a new non-iterative 2DPCA algorithm based on the classification. The effectiveness of the proposed algorithm is experimentally demonstrated on three publicly accessible face image databases.