在基于贝叶斯模型的集群中逃避维度的诅咒
Noirrit Kiran Chandra1, Antonio Canale2, David B Dunson3
1Department of Mathematical Sciences The University of Texas at Dallas Richardson, TX, USA.
贝叶斯混合模型在高维数据聚类方面遇到了困难. 本研究解释了为什么,并介绍了贝叶斯集群 (Lamb) 的潜混合物,以克服这些维度挑战.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 贝叶斯混合模型是集群高维数据的标准.
- 高维度可以导致后置推理中错误的集群计数.
研究的目的:
- 解释贝叶斯聚类产生太多或太少高维的聚类的趋势.
- 提出一种新的贝叶斯聚类方法,解决高维度问题.
主要方法:
- 在固定的样本,增大维度设置中对随机分区后部进行分析.
- 开发用于贝叶斯聚类 (Lamb) 的潜混合物,使用低维潜变量.
- 适用于拟议模型的可扩展的后置推理技术.
主要成果:
- 确定了后推论的条件,随着维度的增加,有利于极端集群 (全部单独或全部在一起).
- 证明这些条件是独立于先前选择的.
- 展示了Lamb在温和假设下避免高维度陷的能力.
结论:
- 贝叶斯聚类 (Lamb) 拟议的潜混合物为高维数据提供了强大的解决方案.
- 兰姆在模拟和现实应用中表现出强的性能,例如单细胞RNA测序数据分析.
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