对视觉数据的张量轮完成,在隐性空间上具有稀疏性和光滑性
1School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China.
概括
张量轮分解用于张量完成面临由于排名选择的过拟合挑战. 这项研究引入了一种分析稀疏性和光滑性的新型模型,以防止过拟合,提高张量完成性能.
科学领域:
- 多变量微积分是多变量微积分.
- 机器学习 机器学习
- 数据科学是数据科学.
背景情况:
- 张量轮分解为探索张量完成中的内在关系提供了优势.
- 张量轮模型中的排名选择可能导致过拟合,特别是在排名敏感的场景中.
研究的目的:
- 在张量轮分解中理论分析稀疏性,光滑性和过拟合性之间的关系.
- 提出一种新的张量完成模型,通过在潜在空间上结合稀疏性和光滑性来减轻过拟合.
主要方法:
- 在张量轮结构内对过的稀疏性和光滑性影响的理论分析.
- 开发一种新的张量完成模型,利用潜在空间的稀疏性和光滑性.
- 使用高效的交替方向方法的乘数 (ADMM) 基于算法的提议模型的优化.
主要成果:
- 拟议的张量完成方法与现有技术相比,表现出优越的性能.
- 该模型在广泛的排名选择中保持了强大的结果.
- 该方法显示,随着等级的增加,对过度装配的易感性降低了.
结论:
- 新型张量轮完成模型有效地解决了与排名选择相关的过拟合问题.
- 纳入稀疏性和流性原则可以提高模型的稳定性和性能.
- 基于ADMM的优化确保了一个高效和实用的解决方案,以完成张量.
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