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Communication Dans Un Congrès Année : 2023

GRAPHIT: Iterative reweighted l1 algorithm for sparse graph inference in state-space models

Résumé

State-space models (SSMs) are a common tool for modeling multi-variate discrete-time signals. The linear-Gaussian (LG) SSM is widely applied as it allows for a closed-form solution at inference, if the model parameters are known. However, they are rarely available in real-world problems and must be estimated. Promoting sparsity of these parameters favours both interpretability and tractable inference. In this work, we propose GraphIT, a majorization-minimization (MM) algorithm for estimating the linear operator in the state equation of an LG-SSM under sparse prior. A versatile family of non-convex regularization potentials is proposed. The MM method relies on tools inherited from the expectationmaximization methodology and the iterated reweighted-l1 approach. In particular, we derive a suitable convex upper bound for the objective function, that we then minimize using a proximal splitting algorithm. Numerical experiments illustrate the benefits of the proposed inference technique.
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Dates et versions

hal-04094496 , version 1 (11-05-2023)

Identifiants

Citer

Emilie Chouzenoux, Víctor Elvira. GRAPHIT: Iterative reweighted l1 algorithm for sparse graph inference in state-space models. ICASSP 2023 - IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2023, Rhodes (Grèce), Greece. ⟨10.1109/ICASSP49357.2023.10096021⟩. ⟨hal-04094496⟩
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