TY - JOUR AU - Yamada, Makoto AU - Sugiyama, Masashi PY - 2011/08/04 Y2 - 2024/03/28 TI - Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 25 IS - 1 SE - AAAI Technical Track: Machine Learning DO - 10.1609/aaai.v25i1.7905 UR - https://ojs.aaai.org/index.php/AAAI/article/view/7905 SP - 549-554 AB - <p> 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 existing density-ratio estimators in terms of both accuracy and computational costs. </p> ER -