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

• Type: Journal
• Year: 2010
• Where: IEICET
• Authors: Makoto Yamada, Masashi Sugiyama, Gordon Wichern, Jaak Simm
• Keywords: IEICET 2010, Importance Estimation, Principal Component Analyzers, Probabilistic Principal Component