为了相似性检索,对对不同的和不确定的梯度采样
1Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, Chile.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
这项研究引入了一种新方法,即双向多样化和不确定梯度 (PairDUG),用于在体育轨迹数据上训练机器学习模型. PairDUG显著减少了计算时间,同时提高了体育分析中的游戏检索质量.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 运动分析 运动分析
背景情况:
- 体育跟踪产生了大量的,非结构化的轨迹数据,对于分析比赛至关重要.
- 目前对这些数据的相似性搜索方法依赖于维度缩小,通常使用罗网络.
- 训练罗网络在计算上昂贵,因为对对比和距离计算的组合性质.
研究的目的:
- 为了应对训练姆网络的计算挑战,进行体育轨迹数据分析.
- 开发一种新的抽样技术,以提高代表性学习的效率和有效性.
- 为了提高相似性搜索的质量和速度,在大型体育数据集中识别有趣的游戏.
主要方法:
- 提出了一种新的采样技术,称为双向多样化和不确定的梯度 (PairDUG).
- PairDUG利用模型梯度信号来选择用于培训的信息和代表性对.
- 在大型篮球和美国足球轨迹数据集上实施和评估PairDUG.
主要成果:
- PairDUG至少将训练所需的计算时间减少了一半.
- 与现有方法相比,检索质量保持或改善.
- 在效率和检索有效性方面表现优于其他基线采样技术.
- 通过PairDUG选择的对显示出更大的梯度大小,多样性和稳定性.
结论:
- PairDUG为高效的双向远程学习提供了基础性的贡献.
- 该方法显著提高了对大型轨迹数据集的训练的计算可行性.
- 未来的工作可以将PairDUG扩展到其他运动和复杂轨迹数据领域.
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