关键词: 核方法, 非线性变换, 特征提取, 互信息

Abstract: Linear transformation can not better retain the nonlinear structure of data，but the nonlinear transformation often requires lots of complex measurements.To address this，a fast and effective method of nonlinear feature extraction is proposed.This method studies the linear invariance of mutual information gradient in the kernel space，and employs a fast algorithm for mutual information and gradient ascent.In this way，the extracted features can reflect the characteristics of discriminative higher-order statistics，and effectively reduce the computational complexity.Detailed data projection and classification experiments show that the proposed approach performs well in classification performance，and is better than traditional nonlinear algorithms for the time complexity.

Key words: kernel method, nonlinear transformation, feature extraction, mutual information