Invariance of mutual information gradient based nonlinear feature extraction
一种互信息梯度不变的非线性特征提取方法*

This paper proposed a fast and effective method of nonlinear feature extraction by studying the linear invariance of mutual information gradient in the linear mutual information feature extraction. It employed a fast algorithm for mutual information and gradient ascent which avoid the eigenvalue decomposition of the traditional nonlinear transformation. In this way, the extracted features could reflect the characteristics of discriminative higher-order statistics, and effectively reduce the computational complexity. Experiments with the UCI read data show that the proposed approach performs well in projection and classification performance, and is better than traditional nonlinear algorithms for the time complexity.