Direct importance estimation with probabilistic principal component analyzers

The importance estimation problem (estimating the ratio of two probability density functions) has recently gathered a great deal of attention for use in various applications, e.g., outlier detection and covariate shift adaptation. In this paper, we propose a new importance estimation method using mixtures of probabilistic principal component analyzers (PPCAs). Our method employs the framework of the KullbackLeibler importance estimation procedure (KLIEP) using using linear or kernel models. The proposed approach entitled PPCA mixture KLIEP (PM-KLIEP) can improve importance estimation accuracy with correlated and rank-deficient data. Through experiments, we show the validity of the proposed approach.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Makoto Yamada, Masashi Sugiyama, Gordon Wichern
• Keywords: ICASSP 2010, Importance Estimation, Importance Estimation Method, Importance Estimation Problem, Signal Processing