Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan.
Neural Netw. 2011 Mar;24(2):183-98. doi: 10.1016/j.neunet.2010.10.005. Epub 2010 Oct 21.
Methods for directly 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, and feature selection. In this paper, we develop a new method which incorporates dimensionality reduction into a direct density-ratio estimation procedure. Our key idea is to find a low-dimensional subspace in which densities are significantly different and perform density-ratio estimation only in this subspace. The proposed method, D(3)-LHSS (Direct Density-ratio estimation with Dimensionality reduction via Least-squares Hetero-distributional Subspace Search), is shown to overcome the limitation of baseline methods.
最近，人们积极探索直接估计两个概率密度函数之比的方法，因为它们可用于各种数据处理任务，如非平稳性适应、异常值检测和特征选择。在本文中，我们开发了一种新方法，将降维纳入直接密度比估计过程中。我们的主要思想是找到一个密度差异显著的低维子空间，并仅在这个子空间中进行密度比估计。所提出的方法 D(3)-LHSS（通过最小二乘异分布子空间搜索进行的带降维的直接密度比估计）被证明克服了基线方法的局限性。
