多视图离散聚类:一个简洁的模型模型
IEEE transactions on pattern analysis and machine intelligence
|September 27, 2023
概括
本研究介绍了多视图离散聚类 (MDC),这是一种新的图形聚类方法,直接解决了原始问题. MDC有效地整合了多视图信息,避免了后处理以获得更优质的集群结果.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算机科学 计算机科学
背景情况:
- 现有的基于图形的多视图集群通常使用两阶段的方法,包括自身分解和后处理.
- 这两个阶段的过程可以导致从直接解决原始集群问题的偏差.
- 从多个视图中有效整合信息对于提高多视图聚类性能至关重要.
研究的目的:
- 提出一个简洁的模型,多视图离散集群 (MDC),直接解决多视图图表集群的主要问题.
- 通过自动权衡特定视图相似度矩阵并直接获得离散指示矩阵来增强多视图集群.
- 为简化和有效的多视图图表集群开发一个没有超参数的模型.
主要方法:
- 开发了多视图离散集群 (MDC) 模型,以直接解决原始多视图图集群问题.
- 实现了视图特定相似度矩阵的自动加权.
- 直接从聚合相似性矩阵中获得离散指标矩阵,没有后处理.
- 设计了一个高效的优化算法来解决目标问题.
主要成果:
- 拟议的MDC模型直接获得离散集群指标,没有后处理.
- 通过自动相似度矩阵权重,MDC有效地整合了来自多个视图的信息.
- 该模型在没有添加组件或可调节的超参数的情况下工作.
- 广泛的实验证明了MDC在合成和真实基准数据集上的优越性.
结论:
- 多视图离散集群 (MDC) 模型为基于图形的多视图集群提供了一种优越的方法.
- 直接解决原始问题,避免后处理,可以提高集群精度.
- 该模型的超参数自由性质和高效的优化增强了其实际应用性.
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