Name
Abstract | ||
|---|---|---|
Inspired by the Compressive sensing (CS) theory, the Lp regularization methods have attracted a great attention. The Lp regularization is a generalized version of the well-known L1 regularization for sparser solution. In this paper, we derive a general thresholding representation for the Lp (0 <; p <; 1) regularization problem in term of a recursive function, which can be well approximated by few steps. This representation can be simplified to the well-known soft-threshold filtering for L1 regularization, the hard-threshold filtering for L0 regularization, and the recently reported half-threshold filtering for L1/2 regularization. This general threshold representation can be easily incorporated into the iterative thresholding framework to provide a tool for sparsity problems. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ISBI.2014.6867844 | ISBI |
Keywords | DocType | ISSN |
recursive function,L0 regularization,L1 regularization,Lp regularization,general thresholding representation,least square,hard-threshold filtering,Compressive sensing,compressive sensing theory,soft-threshold filtering,iterative thresholding framework,compressed sensing,half-threshold filtering,thresholding representation,L2 regularization,sparsity problems,sparsity,Lp regularization method,medical image processing | Conference | 1945-7928 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hengyong Yu | 1 | 12 | 2.27 |
| Chuang Miao | 2 | 0 | 0.34 |