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2010

Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers

8 years 9 months ago
Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers
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. Keywords Importance, Kullback-Leibler importance estimation procedure, Mixture of probabilistic principal component analyzers, Expectation-maximization algorithm
Makoto Yamada, Masashi Sugiyama, Gordon Wichern, J
Added 26 Jan 2011
Updated 26 Jan 2011
Type Journal
Year 2010
Where IEICET
Authors Makoto Yamada, Masashi Sugiyama, Gordon Wichern, Jaak Simm
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