用图结构拼接理论让复杂序列分析更高效，用于滑冰跳跃动作分类
Wiring diagram extraction and gluing: a case study in classifying figure skating jumps using 3D dataset
- 提出图结构拼接理论，解决哈塞聚类算法的组合爆炸问题
- 在3D滑冰动作数据集上实现跳躍类型准确分类
- 适合做时序模式分析与运动识别的研究者参考
哈塞聚类是一种从序列数据中提取共性模式并以图形形式表示的算法。然而，随着预期聚类数量增加，由于组合复杂性，该算法可能变得不可行。本文描述了一种图结构拼接理论，使哈塞聚类可迭代应用，达到单次运行的等效结果。我们在滑冰跳跃视频分类任务中验证了该理论的有效性。
原文摘要 · Abstract (English)
Hasse clustering is an algorithm that extracts common patterns in sequential data and represents them in graphical forms. As the number of expected clusters grows, however, the algorithm can become infeasible to run due to combinatorial complexity. In this article, we describe a theory of gluing wiring diagrams, allowing iterative applications of Hasse clustering to achieve the same result as a single application. We test our theory in the context of classifying videos of figure skating jumps.
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