语义不变多视图集群与完全不完整的信息
IEEE transactions on pattern analysis and machine intelligence
|November 15, 2023
概括
这项研究介绍了SeMantic不变学习 (SMILE),这是一种使用不完整数据进行多视图聚类的新方法. 在不需要配对样本的情况下,SMILE有效地集群数据,克服了现有方法的局限性.
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:
- 在现实世界中,多视图学习通常会受到不完整的信息的影响,例如缺失的对应函数和实例.
- 目前的方法通常需要配对样本来进行数据校正,由于数据收集的复杂性,这往往是不切实际的.
研究的目的:
- 开发一个多视图聚类的新框架,解决不完整的信息,而不依赖于配对样本.
- 在视图中利用不变语义分布来学习共识语义,并提高集群性能.
主要方法:
- 拟议的语义不变学习 (SMILE) 框架识别了不同视角的不变语义分布.
- 微笑缓解交叉视图差异,以学习共识语义,这是强大的分布转移.
- 然后使用这些共识语义,例如重新调整/归纳和集群形成.
主要成果:
- 与五个基准数据集中的13个最先进的基线相比,SMILE表现出了优异的表现.
- 在NoisyMNIST数据集上,SMILE显著提高了集群精度,从19.3%/23.2%提高到82.7%/69.0%,在完全不完整的对应/实例的情况下.
- 该框架有效地处理不完整的对应函数和没有配对数据的不完整实例.
结论:
- 微笑提供了一个强大的解决方案,用于多视图集群与不完整的信息,消除对比样本的需要.
- 该方法学习不变语义分布的能力为处理交叉视图差异和数据缺陷提供了一种强大的方法.
- 显著的性能增长凸显了SMILE在复杂的现实场景中实际应用的潜力.
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