Compressed signal reconstruction using the correntropy induced metric

Sohan Seth, José Principe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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 languageEnglish
Title of host publicationAcoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)978-1-4244-1484-0
ISBN (Print)978-1-4244-1483-3
Publication statusPublished - 31 Mar 2008


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