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Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers
Makoto YAMADA Masashi SUGIYAMA Gordon WICHERN Jaak SIMM
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2010/10/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Fundamentals of Information Systems
importance, Kullback-Leibler importance estimation procedure, mixture of probabilistic principal component analyzers, expectation-maximization algorithm,
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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.