在线追踪高阶不完整流量张量器的低级近似值
Le Trung Thanh1,2, Karim Abed-Meraim1,3, Nguyen Linh Trung2
1PRISME Laboratory, University of Orléans, INSA CVL, 12 Rue de Blois, 45100 Orléans, France.
Patterns (New York, N.Y.)
|July 6, 2023
概括
我们介绍了两个新的算法,自适应式塔克分解 (ATD) 和ACP,用于高效的在线低级张量近似与缺失的数据. 这些方法为流动张量分解提供了快速的融合和低内存使用.
科学领域:
- 数据科学是数据科学.
- 应用数学 应用数学 应用数学
- 计算机科学 计算机科学
背景情况:
- 高阶张量分解对于分析复杂数据集至关重要.
- 追踪缺少数据的流量张量器的低级近似值,带来了重大的计算挑战.
研究的目的:
- 提出两个新的,可证明的算法,用于在线低级近似高阶流量张量与缺失的条目.
- 为了确保这些算法的高效计算,快速融合和低内存要求.
主要方法:
- 适应式塔克分解 (ATD):使用交替最小化框架和随机素描来实现高效的张量因子和核心张量计算.
- 适应式正数多态 (ACP):为正数多态模型量身定制的ATD的一个变体,其中核心张量被限制为同一张量.
- 统一的趋同分析,以严格证明ATD和ACP的表现.
主要成果:
- ATD和ACP都表现出流张量近似的低复杂性和高效跟踪.
- 这些算法表现出快速的融合率和最小的内存存储需求.
- 合成和真实数据的实验结果显示,与现有方法相比,在估计准确性和运行时间方面表现出色.
结论:
- 拟议的ATD和ACP算法提供了有效的解决方案,用于在线低级张量分解缺少数据.
- 这些算法适用于高阶流量张量数据的高效处理.
- 统一的收分析为它们在实际应用中的表现提供了理论上的保证.
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