IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Regular Section
Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers
Makoto YAMADAMasashi SUGIYAMAGordon WICHERNJaak SIMM
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2010 Volume E93.D Issue 10 Pages 2846-2849

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Abstract

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.

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© 2010 The Institute of Electronics, Information and Communication Engineers
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