%0 Journal Article
%T New feature extraction method in high-dimensional datamining based on median regression<br>高维数据挖掘中基于中位数回归的特征提取新方法
%A LI Ze-an
%A CHEN Jian-ping
%A ZHAO Wei-hua
%A <br>李泽安
%A 陈建平
%A 赵为华
%J 计算机应用研究
%D 2013
%I 
%X In order to reduce the unfavorable influence of nosie existing to the feature extractionvariable selection, this paper proposed a robust and effictive method based on median regression and dimensional reduction technique of variable selectionregularized estimation. Moreover, it gave the detail of algorithm which possessed the advantage of fast computation. Simulations show that the new method can effictively estimate and select important variables with high correctness. Even in the case of very low signal to noise ratio, this method still has good result compared with other method. It is really an efficient and robust feature extraction method for high-dimensional data mining.
%K high-dimensional data
%K feature extraction
%K variable selection
%K median regression
%K LASSO<br>高维数据
%K 特征提取
%K 变量选择
%K 中位数回归
%K LASSO
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=51EFA6F8AD100243AF08D705FB711569&yid=FF7AA908D58E97FA&vid=340AC2BF8E7AB4FD&iid=0B39A22176CE99FB&sid=5F8BAECF36EB55E2&eid=09D368C679EC819B&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=7