Equivalence structure (ES) extraction enables us to determine correspondence relations within a dataset or between multiple datasets. Applications of ES extraction include the analysis of time series data, preprocessing of imitation learning, and preprocessing of transfer learning. Currently, pairwise incremental search (PIS) is the fastest method to extract ESs; however, a combinatorial explosion can occur when employing this method. In this paper, we show that combinatorial explosion is a problem that occurs in the PIS, and we propose a new method where this problem does not occur. We evaluate the proposed method via experiments; the results show that our proposed method is 39 times faster than the PIS for synthetic datasets where a 20-dimensional ES exists. For the experiment using video datasets, the proposed method enabled us to obtain a 29-dimensional ES, whereas the PIS did not because the memory usage reached its limit when the number of dimensions was 9. In this experiment, the total processing time for our proposed method up to 29 dimensions was 6.3 times shorter than that for PIS up to even 8 dimensions.

等价结构 (ES) 提取使我们能够确定数据集内或多个数据集之间的对应关系。ES提取的应用包括时间序列数据的分析、模仿学习的预处理和迁移学习的预处理。目前，成对增量搜索（PIS）是提取 ES 的最快方法；然而，使用这种方法时可能会发生组合爆炸。在本文中，我们表明组合爆炸是 PIS 中发生的一个问题，并且我们提出了一种不会发生此问题的新方法。我们通过实验评估所提出的方法；结果表明，对于存在 20 维 ES 的合成数据集，我们提出的方法比 PIS 快 39 倍。对于使用视频数据集的实验，