Presentation 2011-05-19
Higher Order Orthogonal Iteration vs. Multilinear Principle Component Analysis for Tensor Subspace Learning
Guifang DUAN, Yen-Wei CHEN,
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Abstract(in English) Tensor subspace learning has been of great interest in computer vision and pattern recognition applications. In this paper, we demonstrate the relationship Higher Order Orthogonal Iteration (HOOI) and Multilinear Principle Component Analysis (MPCA), which are two of the most fundamental and most popular techniques for tensor subspace learning. We also mathematically prove that MPCA is a special case of HOOI and investigate the performance of HOOR on face recognition.
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Keyword(in English) High Order Orthogonal Iteration (HOOI) / Multilinear Principle Component Analysis (MPCA) / Tensor subspace learning / Face recognition
Paper # IE2011-12,PRMU2011-4,MI2011-4
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Conference Information
Committee PRMU
Conference Date 2011/5/12(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Higher Order Orthogonal Iteration vs. Multilinear Principle Component Analysis for Tensor Subspace Learning
Sub Title (in English)
Keyword(1) High Order Orthogonal Iteration (HOOI)
Keyword(2) Multilinear Principle Component Analysis (MPCA)
Keyword(3) Tensor subspace learning
Keyword(4) Face recognition
1st Author's Name Guifang DUAN
1st Author's Affiliation University Research Organization of Science and Engineering, Ritsumeikan University()
2nd Author's Name Yen-Wei CHEN
2nd Author's Affiliation College of Information Science and Engineering, Ritsumeikan
Date 2011-05-19
Paper # IE2011-12,PRMU2011-4,MI2011-4
Volume (vol) vol.111
Number (no) 48
Page pp.pp.-
#Pages 6
Date of Issue