%0 Journal Article
%T Semi-supervised fuzzy learning strategy by using a way of partitioning the outlier instances<br>离群样本划分的半监督模糊学习策略
%A Song Xiaoning
%A Yang Jingyu
%A Yang Xibei
%A <br>宋晓宁
%A 杨静宇
%A 杨习贝
%J 中国图象图形学报
%D 2012
%I 
%X In this paper, a semi-supervised fuzzy learning algorithm based on the partitioning of the outlier feature space is presented. First, a reformative fuzzy LDA algorithm using a relaxed normalized condition is proposed to achieve the distribution information of each sample represented by a fuzzy membership degree, which is incorporated into the redefinition of the scatter matrices. Moreover, we approach the problem of parameter estimation by considering the formulation of the Hopfield neural network. Using this method, the first key step of the fuzzy classification is addressed. Second, considering the negative influences from the outlier instances, we separate the outliers from the whole feature space by means of the distribution information of each sample. The strength of the technique is that it successfully uses the improved fuzzy supervised algorithm as a feature extraction tool, while quantifying those factors that exert influence ons the outlier class assignment, by means of the fuzzy semi-supervised method. Extensive experimental studies conducted on the NUST603, ORL, XM2VTS and FERET face image databases show that the effectiveness of the proposed fuzzy integrated algorithm.
%K feature extraction
%K fuzzy discriminant analysis
%K outlier samples
%K semi-supervised learning
%K image recognition<br>特征抽取
%K 模糊线性鉴别分析
%K 离群样本
%K 半监督学习
%K 图像识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=0EB9A27D923E9166A4FCB423AAF595A9&yid=99E9153A83D4CB11&vid=BCA2697F357F2001&iid=5D311CA918CA9A03&sid=E151839C3C081609&eid=32491EEEE0A8C927&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=22