Article ID Journal Published Year Pages File Type
404527 Neural Networks 2010 16 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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