Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers

• YAMADA Makoto

  Tokyo Institute of Technology

• SUGIYAMA Masashi

  Tokyo Institute of Technology JST PRESTO

• WICHERN Gordon

  MIT Lincoln Lab.

• SIMM Jaak

  Tokyo Institute of Technology

抄録

Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data mining problems. In this paper, we propose a new importance estimation method using a mixture of probabilistic principal component analyzers. The proposed method is more flexible than existing approaches, and is expected to work well when the target importance function is correlated and rank-deficient. Through experiments, we illustrate the validity of the proposed approach.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E93-D (10), 2846-2849, 2010

  一般社団法人 電子情報通信学会

キーワード

• importance
• Kullback-Leibler importance estimation procedure
• mixture of probabilistic principal component analyzers
• expectation-maximization algorithm

詳細情報

• CRID: 1390282679356266752
• NII論文ID: 10027641383
• NII書誌ID: AA10826272
• DOI: 10.1587/transinf.e93.d.2846
• ISSN: 17451361
• ISSN: 09168532
• Web Site: http://www.jstage.jst.go.jp/article/transinf/E93.D/10/E93.D_10_2846/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可