%0 Journal Article
%T (2D)2UFFCA: Two-directional Two-dimensional Unsupervised Feature Extraction Method with Fuzzy Clustering Ability<br>具有模糊聚类功能的双向二维无监督特征提取方法
%A GAO Jun
%A SUN Chang-Yin
%A WANG Shi-Tong
%A <br>皋军
%A 孙长银
%A 王士同
%J 自动化学报
%D 2012
%I 
%X In this paper,based on the principles of the maximum margin criterion(MMC) and by introducing the fuzzy method and the tensor theory into it,a novel matrix model fuzzy maximum margin criterion(MFMMC) is proposed.Also,on the basis of it,a two-directional two-dimensional unsupervised feature extraction method with fuzzy clustering ability((2D)2UFFCA) is constructed.This method can directly realize fuzzy clustering of matrix model data.And it can also achieve the two-directional two-dimensional feature extraction of them,that is,the realization of dimension reduction.At the same time,the adjusting parameter γ in the matrix model fuzzy maximum margin criterion is defined reasonably from the respect of geometry intuition,which is proved theoretically.In order to improve the efficiency of feature extraction,an effective method which can find out the projection matrices of matrix model data is presented.The results of tests show the above advantages of the method.
%K Tensor model
%K two-directional two-dimensional feature extraction
%K matrix model fuzzy maximum margin criterion (MFMMC)
%K fuzzy clustering<br>张量模式
%K 双向二维特征提取
%K 矩阵模式的模糊最大间距判别准则
%K 模糊聚类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=28538CA98C3000787A071953124F7221&yid=99E9153A83D4CB11&vid=16D8618C6164A3ED&iid=E158A972A605785F&sid=811ACA5D3673A764&eid=12AD09BCF4A6E651&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=40