渐进式邻居掩盖的对比学习用于融合式的深度多视图集群
Mingyang Liu1, Zuyuan Yang2, Wei Han3
1School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, 510006, China.
概括
本研究介绍了用于深度多视图集群 (FDMC) 的渐进邻居掩饰对比学习,以改善表示学习. 新的框架通过在融合视图中保持本地结构和共同的语义来增强集群性能.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算机视觉 计算机视觉
背景情况:
- 深度多视图集群 (FDMC) 集成了来自多个数据视图的功能.
- 现有的FDMC方法与视图特定干扰作斗争,阻碍了歧视性结构学习.
研究的目的:
- 为FDMC (PNCL-FDMC) 提出一个新的框架,即渐进邻居掩饰的对比学习.
- 通过减轻视图特定干扰和增强集群结构学习来解决现有的FDMC方法的局限性.
主要方法:
- 采用邻居掩盖的对比学习,在嵌入聚合过程中保留本地结构.
- 通过使用多样性意识的集群结构增强来增强融合视图.
- 使用增强的集群分配和差异来引导个人和融合观点之间的对比对齐.
主要成果:
- 拟议的PNCL-FDMC框架有效地保持了本地结构,并增强了共同的语义.
- 证明了更好的歧视性代表性学习能力.
- 在广泛的实验中,与现有方法相比,实现了优越的集群性能.
结论:
- 通过有效处理视图特定信息,PNCL-FDMC为深度多视图集群提供了强大的解决方案.
- 该框架成功地增强了语义结构对齐,并提高了整体集群精度.
- 突出了邻居蒙面对比学习在多视图表示学习中的潜力.
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