%0 Journal Article
%T Graph Regularized Non-negative Matrix Factorization with Sparseness Constraints<br>稀疏约束图正则非负矩阵分解
%A JIANG Wei
%A LI Hong
%A YU Zhen-guo
%A YANG Bing-ru
%A <br>姜伟
%A 李宏
%A 余霞国
%A 杨炳儒
%J 计算机科学
%D 2013
%I 
%X Nonncgativc matrix factorization(NMI)is based on part feature extraction algorithm which adds nonnegative constraint into matrix factorization. A method called graph regularized non-negative matrix factorization with sparseness constraints(UNMFSC)was proposed for enhancing the classification accuracy. It not only considers the geometric structure in the data representation, but also introduces sparseness constraint to coefficient matrix and integrates them into one single objective function. An efficient multiplicative updating procedure was produced along with its theoretic justificanon of the algorithmic convergence. Experiments on ORI. and MI T-CBCI. face recognition databases demonstrate the effectiveness of the proposed method.
%K Non-negative matrix
%K Graph Regularization
%K Sparse coding<br>非负矩阵
%K 图正则化
%K 稀疏编码
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=F2F3E0E822DEB5B257E2DD362E65D121&yid=FF7AA908D58E97FA&vid=1371F55DA51B6E64&iid=CA4FD0336C81A37A&sid=AD16A18DBD734D13&eid=EB990F3BE2BA93D6&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=0