Abstract
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.
Highlights
In natural language processing, a sentence is a sequence of words, and in computer vision, a video is a sequence of frames, while in bioinformatics, a protein structure is a sequence of amino acids in a polypeptide chain
Unlike the flat vector data of most machine learning problems, sequence data has the following inherent features: (1) Sequence data is varying at the number of items. e flat feature is usually given at a fixed size, while the length of the sequences could be different, due to the sampling process to form the sequence
We study the problem of learning effective ground distance between the items of the two sequences for the purpose of sequence distance comparison
Summary
Unlike the flat vector data of most machine learning problems, sequence data has the following inherent features:. (1) Sequence data is varying at the number of items. E flat feature is usually given at a fixed size, while the length of the sequences could be different, due to the sampling process to form the sequence. (2) Sequence data has a temporal and relational nature. E order of the items in the sequence plays an important role in the understanding of the sequence. Given two sequences of the same items but with different orders, their meaning could be completely different. Is is a critical, different nature different from the flat vector data, where the items of the vector are considered to be independent of each other and their orders are not important for the learning problem Given two sequences of the same items but with different orders, their meaning could be completely different. is is a critical, different nature different from the flat vector data, where the items of the vector are considered to be independent of each other and their orders are not important for the learning problem
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