Hyperparameter-free sparse regression of grouped variables
Kronvall, Ted; Adalbjornsson, Stefan Ingi; Nadig, Santhosh; Jakobsson, Andreas (2017-03-01). Hyperparameter-free sparse regression of grouped variables Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016, 394 - 398. 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. Pacific Grove, United States: IEEE Computer Society
Conference Proceeding/Paper
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Published
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English
Authors:
Kronvall, Ted
;
Adalbjornsson, Stefan Ingi
;
Nadig, Santhosh
;
Jakobsson, Andreas
Department:
Statistical Signal Processing Group
Mathematics (Faculty of Engineering)
Research Group:
Statistical Signal Processing Group
Abstract:
In this paper, we introduce a novel framework for semi-parametric estimation of an unknown number of signals, each parametrized by a group of components. Via a reformulation of the covariance fitting criteria, we formulate a convex optimization problem over a grid of candidate representations, promoting solutions with only a few active groups. Utilizing the covariance fitting allows for a hyperparameter-free estimation procedure, highly robust against coherency between candidates, while still allowing for a computationally efficient implementation. Numerical simulations illustrate how the proposed method offers a performance similar to the group-LASSO for incoherent dictionaries, and superior performance for coherent dictionaries.
Keywords:
convex optimization ;
covariance fitting ;
group sparsity ;
multi-pitch estimation ;
Probability Theory and Statistics ;
Signal Processing
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