Feature fusion based on Wootters metric

For further enhancing the completeness and conciseness of the existing quantum-inspired feature fusion methods, this paper applies the quantum-related theories of Wootters metric and Fisher linear discriminant to dimension reduction and feature fusion. From the perspective of quantum metric spaces, i.e. phase space and probability space, this paper proposes two different feature fusion methods which take the Wootters statistical distance as the key factor to detect and fuse the duplicate feature data, and are different to the already developed quantum-inspired feature fusion methods. The experimental results reflect the superiority of the proposed feature fusion methods based on the Wootters metric for their better performances on relative completeness and conciseness.