一个灵活的EM-Like集群算法,用于噪音数据
IEEE transactions on pattern analysis and machine intelligence
|November 28, 2023
概括
一个新的灵活的EM类聚类算法 (FEMCA) 能够有效处理非高斯数据,异常值和噪声. 这种聚类方法在现实数据集上胜过传统算法.
科学领域:
- 机器学习 机器学习
- 统计建模 统计建模
- 数据挖掘 数据挖掘
背景情况:
- 对于高斯混合模型的预期最大化 (EM) 算法与非高斯数据,异常值和噪声作斗争.
- 现有的集群方法在复杂的现实场景中往往缺乏稳定性.
研究的目的:
- 引入一种新的集群算法,即灵活的EM类集群算法 (FEMCA).
- 通过适应较重的尾部分布,噪音和异常值来提高集群的稳定性.
主要方法:
- FEMCA采用类似于EM的程序来估计集群中心和协差.
- 一种半参数方法可以估计每个数据点的独特尺度参数.
- 该算法分析了圆分布的独立,非相同分布的样本.
主要成果:
- 对于较重的尾部分布,噪音和异常值,FEMCA显示出强度.
- 该算法表现出重要的无分布属性.
- 使用合成数据分析了收率和准确性.
结论:
- FEMCA的表现明显优于传统的无监督方法,如k-means和标准EM.
- 该算法在基准真实世界数据集 (MNIST,NORB,20newsgroups) 上显示出卓越的性能.
- 对于复杂的集群任务,FEMCA提供了一个更有效和灵活的替代方案.
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