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 · 2010
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.