Efficient Projection for Compressed Sensing

Vo, N; Challa, S; Vo, D; Lee, S

Efficient Projection for Compressed Sensing

Vo, N Challa, S Vo, D Lee, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Conference on Computer and Information Science, 2008. IEEE/ACIS - 7th ICIS, 2008, 1 pp. 322 - 327
Issue Date:: 2008-01

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Field	Value	Language
dc.contributor.author	Vo, N	en_US
dc.contributor.author	Challa, S	en_US
dc.contributor.author	Vo, D	en_US
dc.contributor.author	Lee, S	en_US
dc.contributor.editor	IEEE	en_US
dc.date	2008-05-14	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Proceedings of the International Conference on Computer and Information Science, 2008. IEEE/ACIS - 7th ICIS, 2008, 1 pp. 322 - 327	en_US
dc.identifier.isbn	978-0-7695-3131-1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12645
dc.description.abstract	Compressed sensing (CS), a joint compression and sensing process, is a emerging field of activity in which the signal is sampled and simultaneously compressed at a greatly reduced rate. In CS, the projection matrix is chosen at random which would lead to inefficient performance of CS. Recently, an optimized projection (OP) was chosen such that it leads to better coherence of the effective dictionary, leading substantially better CS reconstruction performance. This is the only recent work related to the optimization of projection matrix, but the major drawback of this approach is that its algorithm is an iterative and high complexity one. It is considered that an efficient and lightweight algorithm for designing projection matrix would usefully supplement and enhance the performance of CS. In this paper, we propose an algorithm to obtain such an projection matrix called efficient projection (EP) which is designed in such a way that the new projected dictionary will have the structure as much similar as the original dictionary. For this purpose, we use multidimensional scaling (MDS) technique which helps to find a low-dimensional new projected dictionary such that the pairwise distances between new atoms in this new projected dictionary match as well as possible the original atoms in original dictionary. This leads to a solution for EP which is very simple and can be obtained by doing singular value decomposition of original dictionary. The experiments show the novelty of our approach when it can gain a comparative performance with a very low complexity compared to that of OP.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of the International Conference on Computer and Information Science, 2008. IEEE/ACIS - 7th ICIS	en_US
dc.relation.ispartof	IEEE/ACIS International Conference on Computer and Information Science	en_US
dc.relation.isbasedon	10.1109/ICIS.2008.72	en_US
dc.rights	© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Efficient Projection for Compressed Sensing	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1	en_US
utslib.location	USA	en_US
utslib.location.activity	Portland, Oregon	en_US
utslib.for	080401 Coding and Information Theory	en_US
dc.location.activity	Portland, Oregon	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	USA	en_US
pubs.start-date	2008-05-14	en_US
pubs.volume	1	en_US

Abstract:

Compressed sensing (CS), a joint compression and sensing process, is a emerging field of activity in which the signal is sampled and simultaneously compressed at a greatly reduced rate. In CS, the projection matrix is chosen at random which would lead to inefficient performance of CS. Recently, an optimized projection (OP) was chosen such that it leads to better coherence of the effective dictionary, leading substantially better CS reconstruction performance. This is the only recent work related to the optimization of projection matrix, but the major drawback of this approach is that its algorithm is an iterative and high complexity one. It is considered that an efficient and lightweight algorithm for designing projection matrix would usefully supplement and enhance the performance of CS. In this paper, we propose an algorithm to obtain such an projection matrix called efficient projection (EP) which is designed in such a way that the new projected dictionary will have the structure as much similar as the original dictionary. For this purpose, we use multidimensional scaling (MDS) technique which helps to find a low-dimensional new projected dictionary such that the pairwise distances between new atoms in this new projected dictionary match as well as possible the original atoms in original dictionary. This leads to a solution for EP which is very simple and can be obtained by doing singular value decomposition of original dictionary. The experiments show the novelty of our approach when it can gain a comparative performance with a very low complexity compared to that of OP.

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http://hdl.handle.net/10453/12645