%0 Journal Article
%T Invariance of mutual information gradient based nonlinear feature extraction<br>一种互信息梯度不变的非线性特征提取方法*
%A XU Hong-zhang
%A NIU Xiao-mei
%A LIAO Hai-bin
%A <br>徐洪章
%A 牛小梅
%A 廖海斌
%J 计算机应用研究
%D 2010
%I 
%X 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.
%K kernel methods
%K nonlinear transformation
%K feature extraction
%K mutual information<br>核方法
%K 非线性变换
%K 特征提取
%K 互信息
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=EDB391C3B6CF352F9FA6CE2D7A380033&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=59906B3B2830C2C5&sid=C4BD8271B8EC6D16&eid=D9041BA492BD44C9&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=17