The Novel Sequence Distance Measuring Algorithm Based on Optimal Transport and Cross-Attention Mechanism

文献类型： 外文期刊

第一作者： Yu, Yanmin

作者： Yu, Yanmin;Lai, Yongcai;Liu, Haiying;Yu, Yanmin;Yan, Ping;Liu, Haiying

作者机构：

期刊名称：SHOCK AND VIBRATION （ 影响因子：1.543； 五年影响因子：1.767 ）

ISSN： 1070-9622

年卷期： 2021 年 2021 卷

页码：

收录情况： SCI

摘要： In this paper, we propose a novel sequence distance measuring algorithm based on optimal transport (OT) and cross-attention mechanism. Given a source sequence and a target sequence, we first calculate the ground distance between each pair of source and target terms of the two sequences. The ground distance is calculated over the subsequences around the two terms. We firstly pay attention from each the source terms to each target terms with attention weights, so that we have a representative source subsequence vector regarding each term in the target subsequence. Then, we pay attention from each representative vector of the term of the target subsequence to the entire source subsequence. In this way, we construct the cross-attention weights and use them to calculate the pairwise ground distances. With the ground distances, we derive the OT distance between the two sequences and train the attention parameters and ground distance metric parameters together. The training process is conducted with training triplets of sequences, where each triplet is composed of an anchor sequence, a must-link sequence, and a cannot-link sequence. The corresponding hinge loss function of each triplet is minimized, and we develop an iterative algorithm to solve the optimal transport problem and the attention/ground distance metric parameters in an alternate way. The experiments over sequence similarity search benchmark datasets, including text, video, and rice smut protein sequence data, are conducted. The experimental results show the algorithm is effective.

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