%0 Journal Article
%T Sparsity Preserving Canonical Correlation Analysis with Application in Feature Fusion<br>稀疏保持典型相关分析及在特征融合中的应用
%A HOU Shu-Dong
%A SUN Quan-Sen
%A <br>侯书东
%A 孙权森
%J 自动化学报
%D 2012
%I 
%X Sparsity preserving projections (SPP) aim to preserve the sparse reconstructive relationship among the data and have been successfully applied in face recognition. The projections are invariant to rotations, rescalings, and translations of the data, and more importantly, they contain natural discriminating information even without class labels. Enlightened by this, we propose a sparsity preserving canonical correlation analysis (SPCCA). It can not only fuse the discriminative information of two feature sets efficiently, but also constrains the sparse reconstructive relationship among each feature set in order to increase the representational power and has good discrimination capability of the feature extracted by SPCCA. Experimental results on the multiple feature databases and face databases show that the proposed SPCCA is better than CCA.
%K Canonical correlation analysis (CCA)
%K sparsity preserving projections (SPP)
%K sparsity preserving CCA (SPCCA)
%K feature fusion<br>典型相关分析
%K (CCA)
%K 稀疏保持投影(SPP)
%K 稀疏保持典型相关分析(SPCCA)
%K 特征融合
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=28538CA98C30007806444CD2CB55C77F&yid=99E9153A83D4CB11&vid=16D8618C6164A3ED&iid=E158A972A605785F&sid=00520952CD4BF212&eid=D559883475316B44&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=29