Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis

Makoto Yamada, Masashi Sugiyama
2011 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Methods for estimating the ratio of two probability density functions have been actively explored recently since they can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, feature selection, and conditional probability estimation. In this paper, we propose a new density-ratio estimator which incorporates dimensionality reduction into the density-ratio estimation procedure. Through experiments, the proposed method is shown to compare favorably with
more » ... existing density-ratio estimators in terms of both accuracy and computational costs.
doi:10.1609/aaai.v25i1.7905 fatcat:blplt2y3abe6tbunrhsvkrqnia