接线图提取与粘合：基于三维数据集的花样滑冰跳跃分类案例研究
Wiring diagram extraction and gluing: a case study in classifying figure skating jumps using 3D dataset
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中文总结 AI 辅助
本文提出接线图粘合理论以解决Hasse聚类的组合复杂度问题，并将其应用于三维数据集的花样滑冰跳跃分类，验证了迭代应用Hasse聚类可获得与单次应用相同结果。
中文摘要 AI 辅助
Hasse聚类是一种提取序列数据中常见模式并以图形形式呈现的算法，但随着预期聚类数量增加，其组合复杂度会导致运行不可行。本文提出了接线图粘合理论，该理论允许迭代应用Hasse聚类以获得单次应用的相同结果。我们在花样滑冰跳跃视频分类场景中对该理论进行了测试。
英文摘要
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.
发表机构
- California State University, Northridge（加州州立大学北岭分校）
机构由 AI 辅助整理，请以论文原文为准。
