A new fuzzy approach for handling class labels in canonical correlation analysis

Canonical correlation analysis (CCA) can extract more discriminative features by utilizing class labels, especially the ones that can reflect the sample distribution appropriately. In this paper, a new fuzzy approach for handling class labels in the form of fuzzy membership degrees is proposed. We elaborately design a novel fuzzy membership function to represent the distribution of image samples. These fuzzy class labels promote the classification performances of CCA and kernel CCA (KCCA) through incorporating distribution information into the process of feature extraction. Comprehensive experimental results on face recognition demonstrate the effectiveness and feasibility of the proposed method.