論文

査読有り
2011年3月

Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search

NEURAL NETWORKS
  • Masashi Sugiyama
  • ,
  • Makoto Yamada
  • ,
  • Paul von Buenau
  • ,
  • Taiji Suzuki
  • ,
  • Takafumi Kanamori
  • ,
  • Motoaki Kawanabe

24
2
開始ページ
183
終了ページ
198
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.neunet.2010.10.005
出版者・発行元
PERGAMON-ELSEVIER SCIENCE LTD

Methods for directly estimating the ratio of two probability density functions have been actively explored recently since they can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, and feature selection. In this paper, we develop a new method which incorporates dimensionality reduction into a direct density-ratio estimation procedure. Our key idea is to find a low-dimensional subspace in which densities are significantly different and perform density-ratio estimation only in this subspace. The proposed method, D-3-LHSS (Direct Density-ratio estimation with Dimensionality reduction via Least-squares Hetero-distributional Subspace Search), is shown to overcome the limitation of baseline methods. (C) 2010 Elsevier Ltd. All rights reserved.

Web of Science ® 被引用回数 : 20

リンク情報
DOI
https://doi.org/10.1016/j.neunet.2010.10.005
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000287910100005&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/journals/nn/nn24.html#journals/nn/SugiyamaYBSKK11
ID情報
  • DOI : 10.1016/j.neunet.2010.10.005
  • ISSN : 0893-6080
  • eISSN : 1879-2782
  • DBLP ID : journals/nn/SugiyamaYBSKK11
  • Web of Science ID : WOS:000287910100005

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