Auto-weighted Sequential Wasserstein Distance and Application to Sequence Matching
Auto-weighted Sequential Wasserstein Distance and Application to Sequence Matching
复制标题
DOI：
10.23919/eusipco55093.2022.9909780
复制
发表时间：
2022-08
期刊：
影响因子：
--
通讯作者：
Mitsuhiko Horie;Hiroyuki Kasai
中科院分区：
文献类型：
--
作者：
Mitsuhiko Horie;Hiroyuki Kasai
几十年来，序列匹配问题一直是数据分析领域的核心。这些问题出现在广泛的不同领域，包括计算机视觉，语音处理，生物信息学和自然语言处理。但是，有效地解决此类问题是困难的，因为人们必须考虑时间一致性，邻域结构相似性，与噪声和离群值的稳健性以及对开始端匹配点的灵活性。本文介绍了在最佳运输（OT）框架建立的序列之间的形状感知瓦斯汀距离的建议。提议的距离考虑了元素，邻居结构和时间位置的相似性度量。我们将这些相似性度量纳入了OT公式的三个地面成本矩阵中。值得注意的贡献是，我们将这些措施作为独立的OT距离，具有单个共享最佳传输矩阵的独立距离，并根据它们对总OT距离的影响自动调整这些权重。数值评估表明，使用我们所提出的Wasserstein距离的序列匹配方法稳健地优于不同现实世界数据集的最先进方法。
Sequence matching problems have been central to the field of data analysis for decades. Such problems arise in widely diverse areas including computer vision, speech processing, bioinformatics, and natural language processing. However, solving such problems efficiently is difficult because one must consider temporal consistency, neighborhood structure similarity, robustness to noise and outliers, and flexibility on start-end matching points. This paper presents a proposal of a shape-aware Wasserstein distance between sequences building upon optimal transport (OT) framework. The proposed distance considers similarity measures of the elements, their neighborhood structures, and temporal positions. We incorporate these similarity measures into three ground cost matrixes of the OT formulation. The noteworthy contribution is that we formulate these measures as independent OT distances with a single shared optimal transport matrix, and adjust those weights automatically according to their effects on the total OT distance. Numerical evaluations suggest that the sequence matching method using our proposed Wasserstein distance robustly outperforms state-of-the-art methods across different real-world datasets.