FC$^{2}$:快速协集群与小规模相似度图和二分位图学习
IEEE transactions on pattern analysis and machine intelligence
|February 6, 2026
概括
快速协集 (FC2) 通过共同建模样本和之间的本地和全球相关性来改善双边图的协集. 这种新的方法提高了基准数据集的聚类性能和效率.
科学领域:
- 数据挖掘 数据挖掘
- 机器学习 机器学习
- 图形理论 图形理论
背景情况:
- 双部分图形协集群方法通常将图形构造与伪标签学习脱,从而导致低于最佳性能.
- 现有的方法忽视了之间的局部多元关系,降低了和样本伪标签的质量.
研究的目的:
- 提出一个新的快速协集群 (FC2) 模型,共同捕捉样本和之间的本地和全球相关性.
- 通过整合自适应式双部分图形构造和伪标签学习来提高协集群的性能.
主要方法:
- 构建一个带有适应性更新权重的双部分图,以建模样和伪标签之间的合.
- 整合一个平衡的规范化术语,从伪标签协差最大化和集群比例平衡之间的等价性得出.
- 通过对相似度图的低级分解来保持伪标签的局部光滑性.
主要成果:
- 拟议的FC2模型与基准和合成数据集的最先进方法相比,显示出更高的性能和效率.
- 共同建模本地和全球相关性导致更合理,更准确的集群分配.
- 适应性图表权重和平衡的规范化增强了协集群过程的稳定性.
结论:
- 通过解决现有方法的局限性,FC2模型为双边图形协集群提供了一个有效的解决方案.
- 样本对应关系和集群平衡的联合优化导致显著的性能改进.
- 开发的代算法确保了大型数据集的高效计算和可扩展性.
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