Topological regularization with information filtering networks

This paper introduces a novel methodology to perform topological regularization in multivariate probabilistic modeling by using sparse, complex, networks which represent the system's dependency structure and are called information filtering networks (IFN). This methodology can be directly appli...

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Bibliographic Details
Main Author:	Aste, T. (Author)
Format:	Article
Language:	English
Published:	Elsevier Inc. 2022
Subjects:	Chow-liu tree Chow-Liu Trees Complex networks Complex systems Covariance selection Expectation Maximization IFN regression Information filtering Information filtering network Information filtering network regression Information filtering networks Inverse covariance Inverse problems Maximum principle Probability distributions Regression analysis Regularisation Sparse expectation-maximization Sparse inverse covariance Topological regularization Topology
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Topological regularization with information filtering networks

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