一个概括的贝叶斯框架用于概率集群
Tommaso Rigon1, Amy H Herring2, David B Dunson2
1Department of Economics, Management and Statistics, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milano, Italy.
本研究引入了集群的泛化贝叶斯框架,为k-means等方法提供不确定性量化. 它将基于损失和基于模型的方法相结合,使得数据集群和分析具有稳定性.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 基于损失的聚类 (例如,k-means) 缺乏不确定性量化.
- 基于模型的集群面临着计算挑战和内核敏感性.
研究的目的:
- 提出一个一般化的贝叶斯集群框架.
- 基于桥梁损失和基于模型的集群模式.
- 为集群方法引入不确定性量化.
主要方法:
- 使用Gibbs posteriors进行贝叶斯更新与损失函数.
- 雇佣布雷格曼分歧和损失定义的对相似之处.
- 开发确定性和采样算法用于估计和不确定性量化.
主要成果:
- 一般化的贝叶斯框架容纳了各种聚类算法,包括k-means.
- 提供了一种量化集群分配不确定性的方法.
- 能够计算数据点聚类概率.
结论:
- 拟议的框架为贝叶斯聚类提供了一种连贯的方法.
- 通过添加不确定性量化来增强现有的集群方法.
- 有助于更可靠的数据分组和解释.
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