論文

査読有り
2007年8月

Joint low-rank approximation for extracting non-Gaussian subspaces

SIGNAL PROCESSING
  • Motoaki Kawanabe
  • ,
  • Fabian J. Theis

87
8
開始ページ
1890
終了ページ
1903
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.sigpro.2007.01.033
出版者・発行元
ELSEVIER SCIENCE BV

In this article, we consider high-dimensional data which contains a low-dimensional non-Gaussian structure contaminated with Gaussian noise. Motivated by the joint diagonalization algorithms, we propose a linear dimension reduction procedure called joint low-dimensional approximation (JLA) to identify the non-Gaussian subspace. The method uses matrices whose non-zero eigen spaces coincide with the non-Gaussian subspace. We also prove its global consistency, that is the true mapping to the non-Gaussian subspace is achieved by maximizing the contrast function defined by such matrices. As examples, we will present two implementations of JLA, one with the fourth-order cumulant tensors and the other with Hessian of the characteristic functions. A numerical study demonstrates validity of our method. In particular, the second algorithm works more robustly and efficiently in most cases. (C) 2007 Elsevier B.V. All rights reserved.

Web of Science ® 被引用回数 : 8

リンク情報
DOI
https://doi.org/10.1016/j.sigpro.2007.01.033
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000246527400008&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/journals/sigpro/sigpro87.html#journals/sigpro/KawanabeT07
ID情報
  • DOI : 10.1016/j.sigpro.2007.01.033
  • ISSN : 0165-1684
  • DBLP ID : journals/sigpro/KawanabeT07
  • Web of Science ID : WOS:000246527400008

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