Quantum inspired method of feature fusion based on von Neumann entropy

One mission of feature fusion is to obtain a complete yet concise presentation of all existing feature data by detecting and fusing the duplicate feature data. In contrast to the already developed feature fusion methods which have shown their limitations, this paper applies the theories of quantum information to feature fusion. Further, a novel and effective step-wise quantum inspired feature fusion method, which detects the duplicate feature data based on maximum von Neumann mutual information and fuses the duplicate feature data using the operations on quantum state, is developed. This same idea is also used for feature dimensionality reduction, and the corresponding models are investigated. For comparison, another quantum inspired feature fusion method based on average quantum phase is presented here. The experimental results show that the quantum inspired feature fusion method based on von Neumann entropy gives better results on completeness and conciseness than the method based on average quantum phase.