Sliced inverse regression with conditional entropy minimization
Sliced inverse regression with conditional entropy minimization
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发表时间：
2012-12
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通讯作者：
H. Hino;Keigo Wakayama;Noboru Murata
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作者：
H. Hino;Keigo Wakayama;Noboru Murata
对原始数据进行适当的降维有助于减少计算时间，揭示复杂数据的内在结构。本文提出了一种回归问题的降维方法。该方法是基于著名的切片逆回归和条件熵最小化。与传统的切片逆回归方法不同，该方法利用熵作为数据分布离散度的度量，在不假设回归函数形式和数据分布的情况下估计降维子空间。通过使用人工和真实世界的数据集进行实验，所提出的方法相比，一些传统的方法表现良好。
An appropriate dimension reduction of raw data helps to reduce computational time and to reveal the intrinsic structure of complex data. In this paper, a dimension reduction method for regression is proposed. The method is based on the well-known sliced inverse regression and conditional entropy minimization. Using entropy as a measure of dispersion of data distribution, dimension reduction subspace is estimated without assuming regression function form nor data distribution, unlike conventional sliced inverse regression. The proposed method is shown to perform well compared to some conventional methods through experiments using both artificial and real-world data sets.