具有适应权重的深度多视图集群,具有统一的尺度表示.
Rui Chen1, Yongqiang Tang2, Wensheng Zhang1
1College of Information Science and Technology, Hainan University, Haikou, 570208, China; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
概括
本研究介绍了具有适应权重的深度多视图集群与统一尺度表示 (AMCU),这是一个用于多源信息集成的新框架. 通过适应性加权视图,AMCU增强了集群,并确保了统一的规模表示,以提高性能.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 多视图集群集成来自多个来源的信息.
- 现有的方法往往无法解释不同的视图重要性和表示规模的统一性.
- 这导致了低于最佳的性能和不清楚的物理意义在集群.
研究的目的:
- 提出一种新的联合学习框架,即适应加权的深度多视图集群与统一尺度表示 (AMCU).
- 解决当前多视图集群方法关于视图重要性和表示规模的局限性.
- 提高多视图集群模型的稳定性和性能.
主要方法:
- 引入了一种适应权重策略,用于视图贡献测量的simplex约束.
- 整合了一种新的调整器,以学习具有大约统一尺度的隐藏表示.
- 开发了一个联合学习框架 (AMCU) 以加强多视图集群.
主要成果:
- 拟议的自适应权重策略为多视图集群提供了明确的物理意义.
- 统一的尺度调节器确保了稳定的模型训练,并降低了对个人观点的敏感性.
- 与最先进的单视图和多视图方法相比,AMCU在八个现实数据集上表现出了优越的性能.
结论:
- 在多视图集群中,AMCU有效地解决了不同视图重要性和非统一的代表性尺度的挑战.
- 该框架为多视图数据分析提供了更稳定,更易于解释的方法.
- 结果显示,与现有方法相比,有了显著的改进,突显了拟议策略的有效性.
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