title: Dimensionality reduction for density ratio estimation in high-dimensional spaces.
abstract: The ratio of two probability density functions is becoming a quantity of interest these days in the machine learning and data mining communities since it can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, and feature selection. Recently, several methods have been developed for directly estimating the density ratio without going through density estimation and were shown to work well in various practical problems. However, these methods still perform rather poorly when the dimensionality of the data domain is high. In this paper, we propose to incorporate a dimensionality reduction scheme into a density-ratio estimation procedure and experimentally show that the estimation accuracy in high-dimensional cases can be improved.
authors.affiliation: Department of Computer Science, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan. sugi@cs.titech.ac.jp
authors.forename: Masashi
authors.initials: M
authors.lastname: Sugiyama
authors.forename: Motoaki
authors.initials: M
authors.lastname: Kawanabe
authors.forename: Pui Ling
authors.initials: PL
authors.lastname: Chui
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016002:Discriminant Analysis; D005820:Genetic Testing; D006801:Humans; D016247:Information Storage and Retrieval; D015233:Models, Statistical; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted