混合多视图数据的综合广义凸聚类优化与特征选择。

Minjie Wang, Genevera I Allen
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引用次数: 0

摘要

在混合多视图数据中,在同一组样本上测量多组不同的特征。通过整合所有可用的数据源,我们试图发现可能隐藏在单个数据视图的个人聚类分析中的样本中的共同群体结构。虽然已经探索了几种用于这种集成聚类的技术,但我们提出并开发了一种凸形式化,它具有很强的经验性能,并继承了日益流行的凸聚类方法的数学特性。具体来说,我们的集成广义凸聚类优化(iGecco)方法为每个不同的数据视图使用不同的凸距离、损失或散度,并使用联合凸融合惩罚来导致共同组。此外,在每个数据源都是高维的情况下,集成混合多视图数据通常很困难。为了在这种情况下进行特征选择,我们开发了一种自适应移位组-套索惩罚,通过将特征缩小到特定于损失的中心来选择特征。我们所谓的iGecco+方法从每个数据视图中选择最适合确定组的特征,这通常导致改进的集成聚类。为了解决我们的问题,我们开发了一种使用子问题近似的新型广义多块ADMM算法,该算法更有效地适合我们的大数据集模型。通过一系列关于文本挖掘和基因组学的数值实验和实际数据实例,我们表明iGecco+在高维混合多视图数据上取得了优异的经验性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data.

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data.

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data.

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data.

In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data view. While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that enjoys strong empirical performance and inherits the mathematical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Optimization (iGecco) method employs different convex distances, losses, or divergences for each of the different data views with a joint convex fusion penalty that leads to common groups. Additionally, integrating mixed multi-view data is often challenging when each data source is high-dimensional. To perform feature selection in such scenarios, we develop an adaptive shifted group-lasso penalty that selects features by shrinking them towards their loss-specific centers. Our so-called iGecco+ approach selects features from each data view that are best for determining the groups, often leading to improved integrative clustering. To solve our problem, we develop a new type of generalized multi-block ADMM algorithm using sub-problem approximations that more efficiently fits our model for big data sets. Through a series of numerical experiments and real data examples on text mining and genomics, we show that iGecco+ achieves superior empirical performance for high-dimensional mixed multi-view data.

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