Fractional-order embedding multiset canonical correlations with applications to multi-feature fusion and recognition
The sample covariance matrices in multiset canonical correlation analysis (MCCA) usually deviate from the true ones owing to noise and the limited number of training samples. In this paper, we thus re-estimate the covariance matrices by using the idea of fractional order embedding to respectively correct sample eigenvalues and singular values. Then, we define fractional-order within-set and between-set scatter matrices, which can significantly reduce the deviation of sample covariance matrices. At last, a novel multiset canonical correlation method is presented for multiset feature fusion, called fractional-order embedding multiset canonical correlations (FEMCCs). The proposed FEMCC method first performs joint feature extraction on multiple sets of feature vectors that are obtained from the same objects, and then fuse the extracted correlation features by a given fusion strategy to form discriminative feature vectors for classification tasks. The proposed method is applied to face recognition and object category classification and is examined using the AR, AT&T, and CMU PIE face image databases and the ETH-80 object database. Numerous experimental results demonstrate the effectiveness and robustness of the FEMCC fusion method.
multiset canonical correlations feature fusion canonical correlation analysis pattern recognition multi-view learning