Name
Abstract | ||
|---|---|---|
In this paper we establish a regularization method for Radon measures. Motivated from sparse L 1 regularization we introduce a new regularization functional for the Radon norm, whose properties are then analyzed. We, furthermore, show well-posedness of Radon measure based sparsity regularization. Finally we present numerical examples along with the underlying algorithmic and implementation details. We shall, here, see that the number of iterations turn out of utmost importance when it comes to obtain reliable reconstructions of sparse data with varying intensities. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02256-2_38 | SSVM |
Keywords | Field | DocType |
show well-posedness,sparsity regularization,sparse data,radon measure,sparse l,radon measures,radon norm,numerical example,implementation detail,reliable reconstruction,regularization method | Hilbert space,Applied mathematics,Mathematical optimization,Radon,Regularization (mathematics),Radon measure,Sparse matrix,Mathematics,Regularization perspectives on support vector machines | Conference |
Volume | ISSN | Citations |
5567 | 0302-9743 | 1 |
PageRank | References | Authors |
0.47 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Otmar Scherzer | 1 | 346 | 52.10 |
| Birgit Walch | 2 | 1 | 0.47 |