Polynomial Graphical Lasso: Learning Edges From Gaussian Graph-Stationary Signals Article Swipe
Andrei Buciulea
,
Jiaxi Ying
,
Antonio G. Marqués
,
Daniel P. Palomar
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tsp.2025.3544376
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tsp.2025.3544376
This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian and stationary on the graph, enabling the development of a graph learning formulation that combines the strengths of graphical lasso with a more encompassing model. Specifically, we assume that the precision matrix can take any polynomial form of the sought graph, allowing for increased flexibility in modeling nodal relationships. Given the inherent complexity and nonconvexity of the optimization problem, we (i) propose a low-complexity algorithm that alternates between estimating the graph and precision matrices, and (ii) characterize its convergence. We evaluate the performance of PGL through comprehensive numerical simulations using both synthetic and real data, demonstrating its superiority over several alternatives. Overall, this approach presents a significant advancement in graph learning and holds promise for various applications in graph-aware signal analysis and beyond. © 1991-2012 IEEE.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tsp.2025.3544376
- OA Status
- hybrid
- Cited By
- 3
- References
- 39
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4407825847Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tsp.2025.3544376Digital Object Identifier
- Title
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Polynomial Graphical Lasso: Learning Edges From Gaussian Graph-Stationary SignalsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Andrei Buciulea, Jiaxi Ying, Antonio G. Marqués, Daniel P. PalomarList of authors in order
- Landing page
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https://doi.org/10.1109/tsp.2025.3544376Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1109/tsp.2025.3544376Direct OA link when available
- Concepts
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Lasso (programming language), Mathematics, Graphical model, Graph, Gaussian, Polynomial, Combinatorics, Algorithm, Computer science, Pattern recognition (psychology), Artificial intelligence, Applied mathematics, Discrete mathematics, Mathematical analysis, Physics, Quantum mechanics, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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