%0 Journal Article
%T A New Robust Subspace Modeling Method<br>一种新的鲁棒子空间建模方法
%A YU Cheng-Wen
%A GUO Lei
%A ZHANG Qian-Jin
%A LI Hui-Hui
%A <br>余成文
%A 郭雷
%A 张前进
%A 李晖晖
%J 计算机科学
%D 2007
%I 
%X Noise data can arbitrarily skew the solution from the desired solution in traditional subspace learning methods that based on least squares estimation,in addition,batch-based mode of subspace learning is time consuming for large scale and high dimensional problem.To deal with these two problems simultaneously,we propose a new robust learning method which comprise of two parts:robust initial parameter learning based on robust dual square function,degrade descending rule and M-estimators,which can automatically detect and remove picture level outliers and pixel level outliers;then followed by a robust incremental subspace learning process considering removing the picture level outliers and pixel level outliers.The simulation experiments show that our method is robust to different noise data,and gets better reconstruction effect of training data through learned subspace for illumination with a much more higher learning speed than Torre's method4].
%K Subspace learning
%K Incremental learning
%K Robust statistic
%K M-estimation
%K Outlier<br>子空间建模
%K 增量学习
%K 鲁棒统计M-估计器
%K 局外点
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=1E8759907DE65478B3D7686003B70BB4&yid=A732AF04DDA03BB3&vid=339D79302DF62549&iid=5D311CA918CA9A03&sid=D5C73DEF4CF8FAF3&eid=8B59EA573021D671&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=8