Abstract
Recovering a sparse signal from insufficient number of measurements has become a popular area of research under the name of compressed sensing or compressive sampling. The reconstruction algorithm of compressed sensing tries to find the sparsest vector (minimum lo-norm) satisfying a series of linear constraints. However, lo-norm minimization, being a NP hard problem is replaced by li-norm minimization with the cost of higher number of measurements in the sampling process. In this paper we propose to minimize an approximation of lo-norm to reduce the required number of measurements. We use the recently introduced correntropy induced metric (CIM) as an approximation of lo-norm, which is also a novel application of CIM. We show that by reducing the kernel size appropriately we can approximate the lo-norm, theoretically, with arbitary accuracy.
| Original language | English |
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| Title of host publication | Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 3845-3848 |
| Number of pages | 4 |
| ISBN (Electronic) | 978-1-4244-1484-0 |
| ISBN (Print) | 978-1-4244-1483-3 |
| DOIs | |
| Publication status | Published - 31 Mar 2008 |