A Supervised Combined Feature Extraction Method for Recognition
نویسندگان
چکیده
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 supervised method of combined feature extraction for multimodal recognition, called discriminative canonical correlation analysis (DCCA), is proposed. The experiments of text categorization, face recognition and handwritten digit recognition show that DCCA outperforms some related methods of both unimodal recognition and multimodal recognition.
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