Sparse additive discriminant canonical correlation analysis for multiple features fusion

Canonical correlation analysis (CCA) is an unsupervised representation learning technique to correlate multi-view data by learning a set of projection matrices. Being complementary with CCA, many discriminant methods are proposed to extract discriminative features of multi-view data by introducing the supervised class information. However, the learned projection matrices in these methods are mathematically constrained to be equal rank to the class number, and thus cannot represent the original data comprehensively. In this paper, we propose a general multi-view information fusion technique, named sparse additive discriminative canonical correlation analysis (SaDCCA). On one hand, SaDCCA is equipped with a strong degree of discrimination by defining a new affinity matrix that reflects the high-order characteristics of intra-class and the separability of inter-class. On the other hand, SaDCCA can exploit the correlation among multi-view data by maintaining the spirit of CCA. The discrimination among classes and the correlation among views are integrated in an additive manner. To obtain the sparse solutions, we first establish the relationship between the objective function and the underdetermined linear system equations, and then obtain the ℓ₁-norm solution by accelerated Bregman iteration with matrix form. SaDCCA has no rank constraint on the projection matrices and is capable to provide accurate recognition performance. Experiments conducted on some publicly available datasets demonstrate the effectiveness of the proposed approach.