Inference of Multiscale Gaussian Graphical Model

• URL: http://arxiv.org/abs/2202.05775v1
• Date: Fri, 11 Feb 2022 17:11:20 GMT
• Title: Inference of Multiscale Gaussian Graphical Model
• Authors: Do Edmond Sanou, Christophe Ambroise and Genevi\`eve Robin
• Abstract summary: We propose a new method allowing to simultaneously infer a hierarchical clustering structure and the graphs describing the structure of independence at each level of the hierarchy. Results on real and synthetic data are presented.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gaussian Graphical Models (GGMs) are widely used for exploratory data analysis in various fields such as genomics, ecology, psychometry. In a high-dimensional setting, when the number of variables exceeds the number of observations by several orders of magnitude, the estimation of GGM is a difficult and unstable optimization problem. Clustering of variables or variable selection is often performed prior to GGM estimation. We propose a new method allowing to simultaneously infer a hierarchical clustering structure and the graphs describing the structure of independence at each level of the hierarchy. This method is based on solving a convex optimization problem combining a graphical lasso penalty with a fused type lasso penalty. Results on real and synthetic data are presented.

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