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医疗数据的无监督学习:概率因数分解方法的审查

Dorien Neijzen1, Gerton Lunter1,2

  • 1Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.

Statistics in medicine
|October 18, 2023
PubMed
概括

本综述统一了流行的无监督学习方法,如主要组件分析和K-means集群,在一个低级别的矩阵因子化框架下. 这澄清了他们对应用医学研究人员分析高维健康数据的假设.

关键词:
集群集成是指集群集成.缩小尺寸缩小尺寸的方法医疗保健研究研究潜变量发现发现.一个概率矩阵的因子化.主题模型 主题模型 主题模型没有监督的学习学习.

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