A New Canonical Correlation Analysis Algorithm with Local Discrimination
Neural Processing Letters
Orientation distance-based discriminative feature extraction for multi-class classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Analysis of Correlation Based Dimension Reduction Methods
International Journal of Applied Mathematics and Computer Science - Issues in Advanced Control and Diagnosis
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Multi-view discriminant analysis
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Kernel sparse locality preserving canonical correlation analysis for multi-modal feature extraction
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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Frontiers of Computer Science: Selected Publications from Chinese Universities
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Multimodal recognition is an emerging technique to overcome the non-robustness of the unimodal recognition in real applications. Canonical correlation analysis (CCA) has been employed as a powerful tool for feature fusion in the realization of such multimodal system. However, CCA is the unsupervised feature extraction and it does not utilize the class information of the samples, resulting in the constraint of the recognition performance. In this paper, the class information is incorporated into the framework of CCA for combined feature extraction, and a novel method of combined feature extraction for multimodal recognition, called discriminative canonical correlation analysis (DCCA), is proposed. The experiments show that DCCA outperforms some related methods of both unimodal recognition and multimodal recognition.