Numerically stable algorithms for adaptive generalized minor subspace extraction (信号処理)
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概要
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This paper presents numerically stable algorithms for adaptive generalized minor subspace extraction. We first propose two algorithms for extracting the generalized eigenvector corresponding to the smallest generalized eigenvalue (i) based on the power method and (ii) by extending the modified Oja-Xu MCA learning algorithm proposed by Peng and Yi ('07). Then, these algorithms are utilized to extract generalized minor subspace in combination with (i) an extension of dimensional reduction technique by Misono and Yamada ('08) and (ii) the Gram-Schmidt process. Numerical examples show that the proposed algorithms are faster and more numerically stable than the reduced-rank generalized eigenvector extraction (R-GEVE) algorithm ('08).
- 2010-02-22
著者
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YAMADA Isao
Department of Physics, Faculty of Science, Chiba University
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NGUYEN Tuan-Duong
Department of Computer Science, Tokyo Institute of Technology
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TAKAHASHI Noriyuki
Global Edge Institute, Tokyo Institute of Technology
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Nguyen Tuan-duong
Department Of Computer Science Tokyo Institute Of Technology
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Yamada Isao
Department Of Communications And Integrated Systems Tokyo Institute Of Technology
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Takahashi Noriyuki
Global Edge Institute Tokyo Institute Of Technology
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Nguyen Tuan-duong
Department Of Communications And Integrated Systems Tokyo Institute Of Technology
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Yamada Isao
Departmen Of Communications And Integrated Systems Tokyo Institute Of Technology
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