Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers

Makoto YAMADA  Masashi SUGIYAMA  Gordon WICHERN  Jaak SIMM  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E93-D   No.10   pp.2846-2849
Publication Date: 2010/10/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E93.D.2846
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Fundamentals of Information Systems
Keyword: 
importance,  Kullback-Leibler importance estimation procedure,  mixture of probabilistic principal component analyzers,  expectation-maximization algorithm,  

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Summary: 
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.