关键词: 广义均值, 均方误差, 典型相关分析, 鲁棒性, 鲁棒典型相关分析

Abstract: Canonical correlation analysis (CCA) is a multivariate statistical analysis method which aims at searching for the linear correlation between two sets of variables of same object. And the criterion function based on L2 norm of minimum mean square error used in CCA results in robustness problem. Generalized mean has been proved to be robust in theory, and has received validation in some applications, such as clustering, object recognition. This paper develops a robust CCA based on generalized mean (GMCCA), which successfully overcomes the drawback that CCA is sensitive to outliers. The method not only inhibits the influence of outliers to achieve robust results, but also avoids the problem of singular covariance matrix in small size of samples. Experiments on multiple feature database (MFD), face database (ORL) and object database (COIL-20) demonstrate the effectiveness of GMCCA.

Key words: generalized mean, mean square error, canonical correlation analysis, robustness, robust canonical correlation analysis