计算机科学与探索2012，Vol.6Issue(8)：708-716,9.DOI:10.3778/j.issn.1673-9418.2012.08.004
核诱导距离度量的鲁棒典型相关分析
Robust Canonical Correlation Analysis Based on Kernel-Induced Measure
摘要
Abstract
Canonical correlation analysis (CCA) is a commonly used multivariate statistical analysis method which aims at searching for the linear correlation between the two sets of variables of the same object. And the Euclidean distance measure used in CCA results in robustness problem. Kernel-induced measure has been proved to be robust in theory, and has been successfully used in clustering. This paper develops a robust CCA based on kernel-induced measure (KI-CCA). It not only overcomes the shortcomings of CCA and some related algorithms which are not robust, but also makes the robust principal component analysis based on maximum entropy be a special case, and has the ability of nonlinear correlation analysis. Because of the diversity of kernel functions, Ki-CCA is a general algorithm. The solution can be obtained by solving a generalized eigenvalue problem as CCA. Experiments on toy problem, multiple feature database (MFD) and face datasets (Yale, AR, ORL) demonstrate the effectiveness of KI-CCA.关键词
典型相关分析(CCA)/核诱导/鲁棒性/广义特征值问题Key words
canonical correlation analysis (CCA)/ kernel-induced/ robustness/ generalized eigenvalue problem分类
信息技术与安全科学引用本文复制引用
丁鑫,陈晓红,陈松灿..核诱导距离度量的鲁棒典型相关分析[J].计算机科学与探索,2012,6(8):708-716,9.基金项目
The National Natural Science Foundation of China under Grant No.61170151(国家自然科学基金) （国家自然科学基金）
the Research Foundation of Nanjing University of Aeronautics and Astronautics under Grant No.NP2011030(南京航空航天大学研究基金) （南京航空航天大学研究基金）
