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- W4361762478 abstract "Due to the periodicity of ECG signal, the time domain energy of the signal accumulates, leading to the iterative mutual interference and suppression, and the concurrent conflict of time-frequency domain features, which requires very high preprocessing and limits the retrieval accuracy and efficiency of the model. Therefore, we introduce hash coding into ECG representation to simplify the complexity of signal representation and improve the efficiency of retrieval. The purpose of hash learning is to expect semantically related data points to share a hash code, that is, a vertex coordinate of Hamming space, but this requirement is a bit harsh and could break the original data structure. Toward this end, we propose a transition-boosted hashing to relax this requirement and map semantically relevant data into a sub-cone of the hypercube, so that each dimensional feature is consistent with the symbol of the hash code and the cosine similarity between the projection point and the hash code is maintained as much as possible. Once the data is mapped to the sub-cone, the desired hash code can be obtained with a simple symbolic operation. The experimental result demonstrates the superiority of presented method competing with state-of-the-art strategies." @default.
- W4361762478 created "2023-04-04" @default.
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- W4361762478 date "2023-01-01" @default.
- W4361762478 modified "2023-09-27" @default.
- W4361762478 title "A Transition-Boosted Hashing for ECG Biometrics" @default.
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- W4361762478 doi "https://doi.org/10.1007/978-981-99-0923-0_92" @default.
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