通过引导标签扩散快速协集群
Fangyuan Xie1, Feiping Nie1, Weizhong Yu1
1School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, PR China.
概括
本研究介绍了通过引导标签扩散 (FCALS) 的快速协集群,这是一个强大的数据集群方法. 通过同时确定样本和标签,FCALS有效地处理噪音数据,提高聚类精度.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 基于图的聚类是有效的,但对杂数据敏感.
- 点连接样品和标签空间,对于标签传播至关重要.
研究的目的:
- 提出一个强大的协集群方法,适应噪音数据.
- 为了同时确定样品和标签,以改进聚类.
主要方法:
- 通过引导标签扩散 (FCALS) 开发了快速协集群.
- 纳入了一个大小约束,以防止微不足道的解决方案,并确保集群有效性.
- 拟用于模糊或离散标签矩阵的连续 (FCALS-C) 和离散 (FCALS-D) 模型.
主要成果:
- FCALS最大限度地提高了之间的集群内部相似性,同时保持了数据关系.
- 尺寸约束在广泛的价值范围内是强大的.
- 在合成和现实世界数据集上的实验结果证明了优越性.
结论:
- FCALS为协集群提供了有效和高效的解决方案,特别是在有噪音数据的情况下.
- 该方法能够直接获得标签,这使得它适合在样本之外的问题.
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