Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6676
Title: Expectation Maximization Based Matching Pursuit
Authors: Gürbüz, Ali Cafer
Pilancı, Mert
Arıkan, Orhan
Keywords: sparse reconstruction
compressive sensing
greedy methods
expectation maximization
Issue Date: 2012
Publisher: IEEE
Source: IEEE International Conference on Acoustics, Speech and Signal Processing -- MAR 25-30, 2012 -- Kyoto, JAPAN
Series/Report no.: International Conference on Acoustics Speech and Signal Processing ICASSP
Abstract: A novel expectation maximization based matching pursuit (EMMP) algorithm is presented. The method uses the measurements as the incomplete data and obtain the complete data which corresponds to the sparse solution using an iterative EM based framework. In standard greedy methods such as matching pursuit or orthogonal matching pursuit a selected atom can not be changed during the course of the algorithm even if the signal doesn't have a support on that atom. The proposed EMMP algorithm is also flexible in that sense. The results show that the proposed method has lower reconstruction errors compared to other greedy algorithms using the same conditions.
URI: https://hdl.handle.net/20.500.11851/6676
ISBN: 978-1-4673-0046-9
ISSN: 1520-6149
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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