Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models

• URL: http://arxiv.org/abs/2102.02431v1
• Date: Thu, 4 Feb 2021 06:13:52 GMT
• Title: Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models
• Authors: Mojtaba Abolfazli, Anders Host-Madsen, June Zhang, Andras Bratincsak
• Abstract summary: We extend description length for data analysis in Gaussian graphical models. Our method uses universal graph coding methods to accurately account for model complexity. Experiments show that our method gives better performance compared to commonly used methods.
• Score: 2.752817022620644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A classic application of description length is for model selection with the minimum description length (MDL) principle. The focus of this paper is to extend description length for data analysis beyond simple model selection and sequences of scalars. More specifically, we extend the description length for data analysis in Gaussian graphical models. These are powerful tools to model interactions among variables in a sequence of i.i.d Gaussian data in the form of a graph. Our method uses universal graph coding methods to accurately account for model complexity, and therefore provide a more rigorous approach for graph model selection. The developed method is tested with synthetic and electrocardiogram (ECG) data to find the graph model and anomaly in Gaussian graphical models. The experiments show that our method gives better performance compared to commonly used methods.

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