論文

査読有り
2010年1月

Dimensionality reduction for density ratio estimation in high-dimensional spaces

NEURAL NETWORKS
  • Masashi Sugiyama
  • ,
  • Motoaki Kawanabe
  • ,
  • Pui Ling Chui

23
1
開始ページ
44
終了ページ
59
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.neunet.2009.07.007
出版者・発行元
PERGAMON-ELSEVIER SCIENCE LTD

The ratio of two prohability density functions is becoming a quantity of interest of these days in the machine learning and data mining communities since it can be used far various data processing tasks such as non-stationarity adaptation, outlier detection, and feature selection Recently. several methods have been developed for directly estimating the de nsity ratio without going through density estimation and were shown to work well in various practical problems. However, these methods still perform rather poorly when the dimensionality of the data domain is high. In this paper, we propose to incorporate a dimensionality reduction scheme into a density-ratio estimation procedure and experimentally show that the estimation accuracy in high-dimensional cases can be improved. (C) 2009 Elsevier Ltd All rights reserved.

Web of Science ® 被引用回数 : 29

リンク情報
DOI
https://doi.org/10.1016/j.neunet.2009.07.007
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000273126500006&DestApp=WOS_CPL
URL
http://dblp.uni-trier.de/db/journals/nn/nn23.html#journals/nn/SugiyamaKC10
ID情報
  • DOI : 10.1016/j.neunet.2009.07.007
  • ISSN : 0893-6080
  • eISSN : 1879-2782
  • DBLP ID : journals/nn/SugiyamaKC10
  • Web of Science ID : WOS:000273126500006

エクスポート
BibTeX RIS