論文

査読有り
2011年9月

Uniqueness of Non-Gaussianity-Based Dimension Reduction

IEEE TRANSACTIONS ON SIGNAL PROCESSING
  • Fabian J. Theis
  • ,
  • Motoaki Kawanabe
  • ,
  • Klaus-Robert Mueller

59
9
開始ページ
4478
終了ページ
4482
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1109/TSP.2011.2159600
出版者・発行元
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Dimension reduction is a key step in preprocessing large-scale data sets. A recently proposed method named non-Gaussian component analysis searches for a projection onto the non-Gaussian part of a given multivariate recording, which is a generalization of the deflationary projection pursuit model. In this contribution, we discuss the uniqueness of the subspaces of such a projection. We prove that a necessary and sufficient condition for uniqueness is that the non-Gaussian signal subspace is of minimal dimension. Furthermore, we propose a measure for estimating this minimal dimension and illustrate it by numerical simulations. Our result guarantees that projection algorithms uniquely recover the underlying lower dimensional data signals.

Web of Science ® 被引用回数 : 6

リンク情報
DOI
https://doi.org/10.1109/TSP.2011.2159600
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000293757300032&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/journals/tsp/tsp59.html#journals/tsp/TheisKM11
ID情報
  • DOI : 10.1109/TSP.2011.2159600
  • ISSN : 1053-587X
  • DBLP ID : journals/tsp/TheisKM11
  • Web of Science ID : WOS:000293757300032

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