Discriminant Feature Fusion Strategy for Supervised Learning

Abstract

An efficient fusion strategy called discriminant feature fusion strategy for supervised learning is proposed to seek the optimal fusion coefficients of feature fusion. Contributions of this paper lie in: 1) creating a constrained optimization problem based on maximum margin criterion for solving the optimal fusion coefficients, which causes that fused data has the largest class discriminant in the fused feature space; 2) keeping an unique solution of optimization problem by transforming the optimization problem to an eigenvalue problem, which causes the fusion strategy to reach a consistent performance. Besides of the detailed theory derivation, many experimental evaluations also are presented in this paper.