TLRLF4MVC:用于可扩展多视图集群的低级别和低频率张量器
概括
本研究引入了一种新的张量低级和低频可扩展多视图集群 (TLRLF4MVC) 方法. TLRLF4MVC通过平衡视图内部相似性和视图之间的相关性来提高集群准确性,有效地处理大型数据集.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 计算机视觉 计算机视觉
背景情况:
- 基于的多视图集群对于大型数据集是有效的.
- 现有的方法在内部视图相似性或计算复杂性方面扎.
- 有效的大规模多视图集群仍然是一个挑战.
研究的目的:
- 开发一种可扩展的多视图集群方法,有效处理大型数据集.
- 为了解决当前方法在视图内相似性和计算成本方面的局限性.
- 为了提高集群精度和效率在大量的多视图数据.
主要方法:
- 引入了一种新的张量低频元件 (TLFC) 运算符,以实现平稳的样本表示.
- 集成的TLFC用于内部视图相似性与张量核规范 (TNN) 和共识规范化用于内部视图相关性.
- 开发了可扩展多视图聚类 (TLRLF4MVC) 的张量低级和低频算法.
- 采用代优化来平衡视图内部相似性和视图补充信息.
主要成果:
- 在6个大规模多视图数据集上,TLRLF4MVC显著超过了最先进的方法.
- 该方法表现出了显著的计算效率,特别是在大规模数据方面.
- 学习的嵌入特征被映射到一个光滑而紧的子空间中,增强聚类性能.
结论:
- TLRLF4MVC为大规模的多视图集群提供了一个计算效率高,准确的解决方案.
- 拟议的TLFC运营商有效地捕捉了视图内部的相似性.
- 整合TNN和共识规范化成功地利用了访视相关性.
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