Multiple-rank supervised canonical correlation analysis for feature extraction, fusion and recognition

The traditional CCA and 2D-CCA algorithms are unsupervised multiple feature extraction methods. Hence, introducing the supervised information of samples into these methods should be able to promote the classification performance. In this paper, a novel method is proposed to carry out the multiple feature extraction for classification, called two-dimensional supervised canonical correlation analysis (2D-SCCA), in which the supervised information is added to the criterion function. Then, by analyzing the relationship between GCCA and 2D-SCCA, another feature extraction method called multiple-rank supervised canonical correlation analysis (MSCCA) is also developed. Different from 2D-SCCA, in MSCCA k pairs left transforms and k pairs right transforms are sought to maximize the correlation. The convergence behavior and computational complexity of the algorithms are analyzed. Experimental results on real-world databases demonstrate the viability of the formulation, they also show that the classification results of our methods are higher than the other's and the computing time is competitive. In this manner, the proposed methods proved to be the competitive multiple feature extraction and classification methods. As such, the two methods may well help to improve image recognition tasks, which are essential in many advanced expert and intelligent systems.