Supervised convex clustering
Supervised convex clustering
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DOI：
10.1111/biom.13860
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发表时间：
2020-05
期刊：
影响因子：
1.9
通讯作者：
Minjie Wang;Tianyi Yao;Genevera I. Allen
中科院分区：
文献类型：
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作者：
Minjie Wang;Tianyi Yao;Genevera I. Allen
聚类长期以来一直是一种流行的无监督学习方法，用于识别相似对象的组，并在许多应用中从未标记的数据中发现模式。然而，对估计的聚类进行有意义的解释往往具有挑战性，这正是由于它们的无监督性质。同时，在许多真实的场景中，存在一些噪声监督辅助变量，例如，主观诊断意见，这些变量与未标记数据的观察到的异质性有关。通过利用来自监督辅助变量和未标记数据的信息，我们试图发现可能被完全无监督分析隐藏的更科学的可解释的群体结构。在这项工作中，我们提出并开发了一种新的统计模式发现方法，称为监督凸聚类（SCC），它从两个信息源中借用力量，并通过联合凸融合惩罚来指导寻找更多可解释的模式。我们开发了SCC的几个扩展来整合不同类型的监督辅助变量、调整额外的协变量并找到双簇。我们通过模拟和阿尔茨海默病基因组学的案例研究证明了SCC的实际优势。具体来说，我们发现了新的候选基因以及阿尔茨海默病的新亚型，这些基因和亚型可能会更好地理解导致老年人认知能力下降异质性的潜在遗传机制。
Clustering has long been a popular unsupervised learning approach to identify groups of similar objects and discover patterns from unlabeled data in many applications. Yet, coming up with meaningful interpretations of the estimated clusters has often been challenging precisely due to their unsupervised nature. Meanwhile, in many real‐world scenarios, there are some noisy supervising auxiliary variables, for instance, subjective diagnostic opinions, that are related to the observed heterogeneity of the unlabeled data. By leveraging information from both supervising auxiliary variables and unlabeled data, we seek to uncover more scientifically interpretable group structures that may be hidden by completely unsupervised analyses. In this work, we propose and develop a new statistical pattern discovery method named supervised convex clustering (SCC) that borrows strength from both information sources and guides towards finding more interpretable patterns via a joint convex fusion penalty. We develop several extensions of SCC to integrate different types of supervising auxiliary variables, to adjust for additional covariates, and to find biclusters. We demonstrate the practical advantages of SCC through simulations and a case study on Alzheimer's disease genomics. Specifically, we discover new candidate genes as well as new subtypes of Alzheimer's disease that can potentially lead to better understanding of the underlying genetic mechanisms responsible for the observed heterogeneity of cognitive decline in older adults.