Expectation maximization based matching pursuit
Date
2012
Authors
Editor(s)
Advisor
Supervisor
Co-Advisor
Co-Supervisor
Instructor
Source Title
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Print ISSN
1520-6149
Electronic ISSN
Publisher
IEEE
Volume
Issue
Pages
3313 - 3316
Language
English
Type
Journal Title
Journal ISSN
Volume Title
Series
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. © 2012 IEEE.
Course
Other identifiers
Book Title
Keywords
Compressive sensing , Expectation Maximization , Greedy algorithms , Greedy method , Incomplete data , Matching pursuit , Orthogonal matching pursuit , Reconstruction error , Sparse reconstruction , Sparse solutions , Channel estimation , Iterative methods , Maximum principle , Signal processing , Algorithms