Abstract
To better reduce redundant data and improve the completeness and conciseness of existing feature data, this articleapplies the theories of fidelity and quantum computation to feature fusion and proposes a novel quantum-inspired method based on maximum fidelity.In contrast tocurrentquantum-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 detectand fuse duplicate feature samples in a subset. Fusion results show that the feature fusion method based on maximum fidelity gives better performances regardingrelative completeness and conciseness than current methods and has wide applications in intelligent systems.
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