伪监督亲和力传播,以实现高效和可扩展的多视图集群.
概括
本研究引入了用于多视图集群的新图构建方法,提高了稳定性和效率. 拟议的伪监督亲和力传播 (PSAP) 框架提高了聚类性能,减少了计算时间.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 基于图的多视图集群 (AGMVC) 提供了效率,但在相似性测量,可扩展性和概括性方面存在局限性.
- 现有的方法难以处理单一结构信息,导致不稳定性和大数据集的高计算成本.
研究的目的:
- 为多视图集群开发一个改进的图构建方法,解决现有方法的局限性.
- 提高多视图集群算法的稳定性,效率和概括能力.
主要方法:
- 提出了一种新的图构建,同时学习本地和全球 (LG) 结构.
- 引入了一个具有里程碑意义的结构的学习方法,消除了图形分区和样本外问题.
- 开发一个伪监督亲和力传播 (PSAP) 框架,共同优化图形构造和里程碑学习,加速融合.
主要成果:
- 该PSAP框架有效地解开了样本和之间的集群内分布.
- 为直接集群输出引入了一个集群推理分区 (CIP) 策略,避免后处理.
- 广泛的实验证实了框架在多视图聚类任务中的效率和有效性.
结论:
- 拟议的LG结构学习和PSAP框架显著推进了基于图的多视图集群.
- 该方法为复杂的集群问题提供了更加稳定,高效和可通用的解决方案.
- 公共可用的代码有助于进一步研究和应用拟议的框架.
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