关键词: 典型相关分析, 特征提取, 局部化判别, 降维

Abstract: In this paper，based on the Canonical Correlation Analysis（CCA），a new supervised learning algorithm called Locality Discriminative CCA（LDCCA） is developed，which introduces the class information of samples and considers the local correlations of both of the within-class samples and the between-class samples.The feature extracted by LDCCA can realize the maximization of local within-class correlations and the minimization of local between-class correlations，which is good for classification of pattern.The experimental results on an artificial dataset，multiple feature database and facial database including ORL，Yale and AR show that the proposed LDCCA can effectively enhance the classification performance by using class information.

Key words: canonical correlation analysis, feature extraction, local discrimination, dimensionality reduction