A Nonconvex Variational Approach for Robust Graphical Lasso.

Abstract : In recent years, there has been a growing interest in problems in graph estimation and model selection, which all share very similar matrix variational formulations, the most popular one being probably GLASSO. Unfortunately, the standard GLASSO formulation does not take into account noise corrupting the data: this shortcoming leads us to propose a novel cost function, where the regularization function is decoupled in two terms, one acting only on the eigenvalues of the matrix and the other on the matrix elements. Incorporating noise information into the model has the side–effect to make the cost function non–convex. To overcome this difficulty, we adopt a majorization–minimization approach, where at each iteration a convex approximation of the original cost function is minimized via the Douglas–Rachford procedure. The achieved results are very promising w.r.t. classical approaches.
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Communication dans un congrès
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2018), Apr 2018, Calgary, Canada. 2018, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2018)
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Soumis le : mercredi 30 mai 2018 - 12:36:54
Dernière modification le : jeudi 5 juillet 2018 - 14:45:32

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Alessandro Benfenati, Emilie Chouzenoux, Jean-Christophe Pesquet. A Nonconvex Variational Approach for Robust Graphical Lasso.. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2018), Apr 2018, Calgary, Canada. 2018, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2018). 〈hal-01803424〉

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