Higher Order Orthogonal Iteration vs. Multilinear Principle Component Analysis for Tensor Subspace Learning

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概要

• 論文の詳細を見る

• 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.

• 2011-05-12

著者

• Chen Yen-wei

  College Of Information And Science Ristumeikan University

• DUAN Guifang

  University Research Organization of Science and Engineering, Ritsumeikan University

• Duan Guifang

  University Research Organization Of Science And Engineering Ritsumeikan University

• 段 桂芳

  University Research Organization of Science and Engineering, Ritsumeikan University

• 陳 延偉

  College of Information Science and Engineering, Ritsumeikan

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