复合注意力机制网络用于深度对比的多视图集群
Tingting Du1, Wei Zheng2, Xingang Xu1
1School of Computer Science, Guangdong University of Science and Technology, Dongguan, 523083, China.
概括
本研究引入了一种新的复合注意力框架,用于深度多视图集群,增强隐藏特征和未标记数据的集群分配之间的表示学习和通信.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 深度多视图集群方法通常使用自动编码器和对比学习.
- 现有的方法往往无法捕捉内在或跨视图的固有关系.
- 有限的对比模块可以阻碍表示和集群分配之间的通信.
研究的目的:
- 为深度多视图集群提出一个新的综合关注框架.
- 解决代表性学习的局限性,并在现有方法中特征沟通.
- 为了提高对未标记的多视图数据的聚类性能.
主要方法:
- 使用一个复合的注意力结构与等级变换器和共享注意力来进行表示学习.
- 实施一个双重对比的框架与一个新的通信损失.
- 增强隐藏的表示,以保持内视特征和平衡交叉视图贡献.
主要成果:
- 与最先进的算法相比,拟议的方法实现了优越的集群性能.
- 在加州理工学院数据集及其子集中观察到显著的准确性改进,平均为10%.
- 该框架有效地挖掘了固有的关系,并加强了代表和集群分配之间的沟通.
结论:
- 复合注意力框架为深度多视图集群提供了更有效的方法.
- 该方法在处理未标记的多视图数据方面表现出强大的性能,特别是在细分任务中.
- 这项工作通过改进表示学习和集群中的特征集成来推进该领域.
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