Dimensionality reduction for density ratio estimation in high-dimensional spaces

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 densityratio estimation procedure and experimentally show that the estimation accuracy in high-dimensional cases can be improved. Keywords density ratio estimation, dimensionality reduction, local Fisher discriminant analysis, unconstrained least-squares importance fitting 

• Type: Journal
• Year: 2010
• Where: NN
• Authors: Masashi Sugiyama, Motoaki Kawanabe, Pui Ling Chui
• Keywords: Dimensionality Reduction, Estimation, Neural Networks, NN 2010, Probability Density Functions