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Learning Model-Based Sparsity via Projected Gradient Descent.
- Source :
-
IEEE Transactions on Information Theory . Apr2016, Vol. 62 Issue 4, p2092-2099. 8p. - Publication Year :
- 2016
-
Abstract
- Several convex formulation methods have been proposed previously for statistical estimation with structured sparsity as the prior. These methods often require a carefully tuned regularization parameter, often a cumbersome or heuristic exercise. Furthermore, the estimate that these methods produce might not belong to the desired sparsity model, albeit accurately approximating the true parameter. Therefore, greedy-type algorithms could often be more desirable in estimating structured-sparse parameters. So far, these greedy methods have mostly focused on linear statistical models. In this paper, we study the projected gradient descent with a non-convex structured-sparse parameter model as the constraint set. Should the cost function have a stable model-restricted Hessian, the algorithm produces an approximation for the desired minimizer. As an example, we elaborate on application of the main results to estimation in generalized linear models. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189448
- Volume :
- 62
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Information Theory
- Publication Type :
- Academic Journal
- Accession number :
- 113872616
- Full Text :
- https://doi.org/10.1109/TIT.2016.2515078