Graph regularized multiset canonical correlations with applications to joint feature extraction
We propose a novel algorithm called GrMCC for joint feature extraction. GrMCC considers both discriminative and intrinsic geometrical structure in multi-representation data. The extracted features by GrMCC have strong discriminant power for recognition. Experimental results show GrMCC can provide encouraging recognition results in contrast to the state-of-the-art algorithms.
multiset canonical correlations feature extraction pattern recognition canonical correlation analysis graph embedding