Aste, T;
(2022)
Topological regularization with information filtering networks.
Information Sciences
, 608
pp. 655-669.
10.1016/j.ins.2022.06.007.
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Abstract
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 applied to covariance selection problem providing an instrument for sparse probabilistic modeling with both linear and non-linear multivariate probability distributions such as the elliptical and generalized hyperbolic families. It can also be directly implemented for topological regularization of multicollinear regression. In this paper, I describe in detail an application to sparse modeling with multivariate Student-t. A specific expectation–maximization likelihood maximization procedure over a sparse chordal network representation is proposed for this sparse Student-t case. Examples with real data from stock prices log-returns and from artificially generated data demonstrate applicability, performances, robustness and potentials of this methodology.
| Type: | Article |
|---|---|
| Title: | Topological regularization with information filtering networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ins.2022.06.007 |
| Publisher version: | https://doi.org/10.1016/j.ins.2022.06.007 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Topological regularization, Information filtering networks, Complex systems, Covariance selection, Sparse inverse covariance, Chow-Liu Trees, Sparse expectation-maximization, IFN regression |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10152324 |
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