論文

査読有り
2011年

An Information Geometrical View of Stationary Subspace Analysis

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2011, PT II
  • Motoaki Kawanabe
  • ,
  • Wojciech Samek
  • ,
  • Paul von Buenau
  • ,
  • Frank C. Meinecke

6792
開始ページ
397
終了ページ
404
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1007/978-3-642-21738-8_51
出版者・発行元
SPRINGER-VERLAG BERLIN

Stationary Subspace Analysis (SSA) [3] is an unsupervised learning method that finds subspaces in which data distributions stay invariant over time. It has been shown to be very useful for studying non-stationarities in various applications [5,10,4,9]. In this paper, we present the first SSA algorithm based on a full generative model of the data. This new derivation relates SSA to previous work on finding interesting subspaces from high-dimensional data in a similar way as the three easy routes to independent component analysis [6], and provides an information geometric view.

Web of Science ® 被引用回数 : 5

リンク情報
DOI
https://doi.org/10.1007/978-3-642-21738-8_51
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000296487200051&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/conf/icann/icann2011-2.html#conf/icann/KawanabeSBM11
ID情報
  • DOI : 10.1007/978-3-642-21738-8_51
  • ISSN : 0302-9743
  • eISSN : 1611-3349
  • DBLP ID : conf/icann/KawanabeSBM11
  • Web of Science ID : WOS:000296487200051

エクスポート
BibTeX RIS