%0 Journal Article
%T Semi-Supervised Canonical Correlation Analysis Algorithm<br>半监督典型相关分析算法
%A PENG Yan
%A ZHANG Dao-Qiang
%A <br>彭岩
%A 张道强
%J 软件学报
%D 2008
%I 
%X In this paper,a semi-supervised canonical correlation analysis algorithm called Semi-CCA is developed, which uses supervision information in the form of pair-wise constraints in canonical correlation analysis (CCA).In this setting,besides abundant unlabeled data examples,the domain knowledge in the form of pair-wise constraints which specify whether a pair of data examples belongs to the same class (must-link constraints) or not (cannot-link constraints) is also available.Meanwhile,the relative importance of must-link constraints and cannot-link constraints is validated.Experimental results on the artificial dataset,multiple feature database and facial database including Yale and AR show that the proposed Semi-CCA can effectively enhance the classifier performance by using only a small amount of supervision information.
%K canonical correlation analysis
%K semi-supervised learning
%K pair-wise constraints
%K dimensionality reduction
%K classification<br>典型相关分析
%K 半监督学习
%K 成对约束
%K 降维
%K 分类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=AA45ED7C39D519DAE1FB618254226E06&yid=67289AFF6305E306&vid=2A8D03AD8076A2E3&iid=708DD6B15D2464E8&sid=B15E352F2A5F8899&eid=54C6FEE55FB2F876&journal_id=1000-9825&journal_name=软件学报&referenced_num=6&reference_num=28