| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 7108218 | Automatica | 2018 | 9 Pages |
Abstract
In this paper, we develop an upper bound for the SPARSEVA (SPARSe Estimation based on a VAlidation criterion) estimation error in a general scheme, i.e., when the cost function is strongly convex and the regularized norm is decomposable for a pair of subspaces. We show how this general bound can be applied to a sparse regression problem to obtain an upper bound of the estimation error for the traditional l1 SPARSEVA problem. Numerical results are used to illustrate the effectiveness of the suggested bound.
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Control and Systems Engineering
Authors
Huong Ha, James S. Welsh, Cristian R. Rojas, Bo Wahlberg,