Liu, Tianyi ; Hoang-Minh, T. ; Yang, Yang ; Pesavento, Marius (2019)
A block Coordinate Descent Algorithm for Sparse Gaussian Graphical Model Interference with Laplacian Constraints.
8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP'19). Guadeloupe, West Indies (15.12.2019-18.12.2019)
doi: 10.1109/CAMSAP45676.2019.9022643
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We consider the problem of inferring sparse Gaussian graphical models with Laplacian constraints, which can also be viewed as learning a graph Laplacian such that the observed graph signals are smooth with respect to it. A block coordinate descent algorithm is proposed for the resulting linearly constrained log-determinant maximum likelihood estimation problem with sparse regularization. Simulation results on synthetic data show the efficiency of our proposed algorithm.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Liu, Tianyi ; Hoang-Minh, T. ; Yang, Yang ; Pesavento, Marius |
| Art des Eintrags: | Bibliographie |
| Titel: | A block Coordinate Descent Algorithm for Sparse Gaussian Graphical Model Interference with Laplacian Constraints |
| Sprache: | Englisch |
| Publikationsjahr: | 19 Dezember 2019 |
| Verlag: | IEEE |
| Buchtitel: | CAMSAP 2019: Proceedings |
| Veranstaltungstitel: | 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP'19) |
| Veranstaltungsort: | Guadeloupe, West Indies |
| Veranstaltungsdatum: | 15.12.2019-18.12.2019 |
| DOI: | 10.1109/CAMSAP45676.2019.9022643 |
| Kurzbeschreibung (Abstract): | We consider the problem of inferring sparse Gaussian graphical models with Laplacian constraints, which can also be viewed as learning a graph Laplacian such that the observed graph signals are smooth with respect to it. A block coordinate descent algorithm is proposed for the resulting linearly constrained log-determinant maximum likelihood estimation problem with sparse regularization. Simulation results on synthetic data show the efficiency of our proposed algorithm. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Nachrichtentechnische Systeme |
| Hinterlegungsdatum: | 01 Nov 2019 13:10 |
| Letzte Änderung: | 15 Nov 2022 10:18 |
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