Abstract Based on the canonical correlation analysis (CCA), a supervised learning algorithm, sparsity preserving CCA with missing samples (SPCCAM), is proposed. The class information of samples is introduced by sparsity preserving and cross correlation is used to overcome the limitations of the CCA and its extensions that the paired samples of different views are required. SPCCAM can combine features from different views with unpaired training samples. The experimental results on the artificial dataset, multiple feature database and PIE facial database show that the proposed SPCCAM effectively enhances the classification performance by using class information.
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2014, Vol. 27 
