通过图学习实现高阶相关性和一致性意识的多视图聚类
Cheng Liang1, Wenchao Zang1, Daoyuan Wang2
1School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan, 250358, Shandong, China.
概括
通过图学习 (HCAGL) 的高顺序相关性和一致性意识多视图集群可以提高计算效率和准确性. 这种新的框架有效地捕捉了大规模异构数据中的高阶相关性和交叉视图一致性.
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数据源的信息.
- 传统方法面临着计算效率,高阶相关性和交叉视图一致性方面的挑战,特别是对于大规模的异构数据.
研究的目的:
- 提出一个新的框架,HCAGL,解决传统多视图集群的局限性.
- 为了提高计算效率,捕捉高阶相关性,并保持交叉视图的一致性.
主要方法:
- 通过一组紧的点来利用图学习来减少维度和提高效率.
- 在低维嵌入上使用张量Schatten p-norm来捕捉高阶相关性并传播全球一致性.
- 结合自适应社区图表学习来进行动态视图特定权重调整,以提高交叉视图的一致性.
主要成果:
- 在六个基准数据集上,HCAGL在捕获交叉视图一致性和高阶相关性方面表现出卓越的表现.
- 与现有的多视图方法相比,实现了更高的准确性和更好的集群质量.
- 组件分析证实了设计选择的积极贡献,确保稳定可靠的结果.
结论:
- HCAGL为复杂的多视图集群提供了有效和计算效率高的解决方案.
- 该框架成功地解决了与大规模和异构的多视图数据相关的挑战.
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