Semi-discriminative Multiview Canonical Correlation Analysis for Recognition

Different with typical supervised canonical correlation methods where intraclass and interclass information of samples are exploited at the same time for classification tasks, in this paper, we follow the principle of Occam’s razor and thus propose a new supervised multiview dimensionality reduction method for image recognition, called semi-discriminative multiview canonical correlations (SemiDMCCs), which takes partial class information into account but generates discriminative low-dimensional projections. Experimental results on benchmark databases show the more effectiveness of the proposed method, in contrast to existing feature reduction methods.

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