通过底层图形过来实现光滑多个内核k-Means
概括
本研究介绍了一种新的光滑多核k-means (SMKKM-UGF) 算法. 它有效地处理内核集群中的噪音和数据结构,优于现有方法.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 没有监督的学习学习.
背景情况:
- 集群是无监督学习的基础.
- 内核方法将集群扩展到非线性问题.
- 多个内核K-Means (MKKM) 结合了内核进行共识集群.
研究的目的:
- 解决现有的MKKM算法的关于噪声和数据结构的局限性.
- 通过底层图形过 (SMKKM-UGF) 提出一种新的光滑MKKM.
主要方法:
- 通过使用底层图形过来学习内核化数据点的平滑表示.
- 共同更新图形过器和平滑内核以进行自适应过.
- 采用一个趋同的代算法进行优化.
主要成果:
- 显示了SMKKM-UGF在最先进的集群方法上的优越性能.
- 通过对基准数据集的广泛实验进行验证.
结论:
- SMKKM-UGF通过考虑噪音和底层数据结构,为内核集群提供了一种有效的方法.
- 拟议的方法提供了强大而准确的集群结果.
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