Discriminant parallel feature fusion based on maximum margin criterion for pattern classification

In this paper, we propose a novel parallel feature fusion based maximum margin criterion, namely discriminant parallel feature fusion, for pattern classification. The advantage of algorithm lies in: 1) A constrained optimization problem based on maximum margin criterion is created to solve the optimal fusion coefficients, which causes that fused data has the largest class discriminant in the fused feature space. 2) An unique solution of optimization problem is transformed to an eigenvalue problem, which causes the proposed fusion strategy to perform a consistent performance. Besides of the detailed theory derivation, many experimental evaluations also are presented in this paper.