Bipartite Structured Gaussian Graphical Modeling via Adjacency Spectral Priors

Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, D. Palomar
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引用次数: 3

Abstract

Learning a graph with a bipartite structure IS essential for interpretability and identification of the relationships among data in numerous applications including document clustering, network medicine, etc. To learn a bipartite structure is equivalent to a max-cut problem, which is an NP-hard problem. Existing methods employ a two-stage procedure and are computationally demanding as they require solving semi-definite programming. In this paper, we introduce a bipartite graph learning framework lying at the integration of Gaussian graphical models (GGM) and spectral graph theory. The proposed algorithms are provably convergent and practically amenable for large-scale unsupervised graph learning tasks. Numerical experiments demonstrate the effectiveness of the proposed algorithm over existing state-of-the-art methods. An R package containing code for all the experimental results is available at https://cran.r-project.org/package=spectralGraphTopology.
基于邻接谱先验的二部结构高斯图形建模
在文档聚类、网络医学等众多应用中,学习具有二部结构的图对于数据之间关系的可解释性和识别至关重要。学习二部结构相当于一个极大切问题,是一个np困难问题。现有的方法采用两阶段程序,并且由于需要求解半确定规划,计算要求很高。本文介绍了一种基于高斯图模型和谱图理论的二部图学习框架。所提出的算法收敛性好,适用于大规模的无监督图学习任务。数值实验证明了该算法比现有的最先进的方法更有效。包含所有实验结果代码的R包可在https://cran.r-project.org/package=spectralGraphTopology上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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