TY - GEN
T1 - A unified view of two-dimensional principal component analyses
AU - Inoue, Kohei
AU - Hara, Kenji
AU - Urahama, Kiichi
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84868089880&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84868089880&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34166-3_62
DO - 10.1007/978-3-642-34166-3_62
M3 - Conference contribution
AN - SCOPUS:84868089880
SN - 9783642341656
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 566
EP - 574
BT - Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR and SPR 2012, Proceedings
T2 - Joint IAPR International Workshops on Structural and Syntactic PatternRecognition, SSPR 2012 and Statistical Techniques in Pattern Recognition,SPR 2012
Y2 - 7 November 2012 through 9 November 2012
ER -