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Graph Laplacian Mixture Model
IEEE Transactions on Signal and Information Processing over Networks ( IF 3.2 ) Pub Date : 2020-03-30 , DOI: 10.1109/tsipn.2020.2983139
Hermina Petric Maretic , Pascal Frossard

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available data relate to the same graph. This is, however, not always the case, as data is often available in mixed form, yielding the need for methods that are able to cope with mixture data and learn multiple graphs. We propose a novel generative model that represents a collection of distinct data which naturally live on different graphs. We assume the mapping of data to graphs is not known and investigate the problem of jointly clustering a set of data and learning a graph for each of the clusters. Experiments demonstrate promising performance in data clustering and multiple graph inference, and show desirable properties in terms of interpretability and coping with high dimensionality on weather and traffic data, as well as digit classification.

中文翻译:

图拉普拉斯混合模型

图学习方法作为一种推断数据集中结构的手段,近年来受到越来越多的关注。最近的大多数方法都集中于图形与数据样本分布之间的不同关系,主要是在所有可用数据都与同一图形相关的设置中。但是,情况并非总是如此,因为数据通常以混合形式提供,因此需要能够处理混合数据并学习多个图形的方法。我们提出了一种新颖的生成模型,该模型表示自然存在于不同图形上的不同数据的集合。我们假设数据到图的映射是未知的,并研究将一组数据联合聚类并为每个聚类学习图的问题。实验证明了在数据聚类和多图推理方面的有希望的性能,
更新日期:2020-04-22
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