Data di Pubblicazione:
2018
Citazione:
A nonconvex variational approach for robust graphical lasso / A. Benfenati, E. Chouzenoux, J.-. Pesquet (PROCEEDINGS OF THE ... IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING). - In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)[s.l] : IEEE, 2018. - ISBN 9781538646588. - pp. 3969-3973 (( convegno IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) tenutosi a Calgary nel 2018 [10.1109/ICASSP.2018.8462421].
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 criterion, 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.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
Majorization-minimization; Graphical LASSO; Non-convex Optimization; Covariance Estimation; Proximal Methods
Elenco autori:
A. Benfenati, E. Chouzenoux, J.-. Pesquet
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Link al Full Text:
Titolo del libro:
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)