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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Selesnick, I.W. Bayram, I. |
| Copyright Year | 1991 |
| Abstract | This paper addresses the problem of sparsity penalized least squares for applications in sparse signal processing, e.g., sparse deconvolution. This paper aims to induce sparsity more strongly than L1 norm regularization, while avoiding non-convex optimization. For this purpose, this paper describes the design and use of non-convex penalty functions (regularizers) constrained so as to ensure the convexity of the total cost function F to be minimized. The method is based on parametric penalty functions, the parameters of which are constrained to ensure convexity of F. It is shown that optimal parameters can be obtained by semidefinite programming (SDP). This maximally sparse convex (MSC) approach yields maximally non-convex sparsity-inducing penalty functions constrained such that the total cost function F is convex. It is demonstrated that iterative MSC (IMSC) can yield solutions substantially more sparse than the standard convex sparsity-inducing approach, i.e., L1 norm minimization. |
| Sponsorship | IEEE Signal Processing Society |
| Starting Page | 1078 |
| Ending Page | 1092 |
| Page Count | 15 |
| File Size | 2593378 |
| File Format | |
| ISSN | 1053587X |
| Volume Number | 62 |
| Issue Number | 5 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Convex optimization Iterative methods non-convex optimization sparse optimization sparse regularization basis pursuit lasso deconvolution L1 norm threshold function |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Electrical and Electronic Engineering |
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