A Quantum-Inspired Feature Fusion Method Based on Maximum Fidelity
Weimin Peng, Ai−Hong Chen, Yong Sun · IEEE Intelligent Systems · 2017
To better reduce redundant data and improve the completeness and conciseness of existing feature data, this article applies the theories of fidelity and quantum computation to feature fusion and proposes a novel quantum-inspired method based on maximum fidelity.In contrast to current quantum-inspired feature fusion methods,this method uses the fidelity between feature samples and takes the maximum fidelity and the maximum component of fidelity as key factors to detect and fuse duplicate feature samples in a subset. Fusion results show that the feature fusion method based on maximum fidelity gives better performances regarding relative completeness and conciseness than current methods and has wide applications in intelligent systems.