Sparse Recovery Using Conjugate Gradient and Orthogonal Triangular Decomposition

Xi Hua Peng; Shan Xiong Chen; Xiao Yan Liu

doi:10.4028/www.scientific.net/AMR.756-759.2479

Paper Titles

System Reliability Evaluation Using Bayesian Networks
p.2457

A Corpus-Based Study of Chunks in Applied Linguistics Research Articles
p.2465

Geo^X/G/1/N Queue with Vacation and Limited Service Discipline
p.2470

The Application and Research of Personalized Network Teaching Resources Database Based on Data Mining
p.2475

Sparse Recovery Using Conjugate Gradient and Orthogonal Triangular Decomposition
p.2479

Semantic Map Model and Construction of Synset
p.2484

The Research of Wine Quality Evaluation Based on Multiple Linear Regression
p.2489

The Realization of MP3 Player System Based on Mediastreamer2 Framework
p.2494

The Application of Rank Related Coefficient
p.2499

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 756-759Sparse Recovery Using Conjugate Gradient and...

Sparse Recovery Using Conjugate Gradient and Orthogonal Triangular Decomposition

Abstract:

In this article, we propose the matching pursuit algorithm of combinatorial optimization based CGLS and LSQR. We use non-negative matrix factorization for measuring discrepancy of solution sequence between CGLS and LSQR, and represent combinatorial optimization based CGLS and LSQ to choose optimal solution sequences. The experiments indicate our method is extended to the case where target signal has been corrupted by noise, it demonstrate perfectly recovery ability of signal with noise.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 756-759)

Pages:

2479-2483

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.756-759.2479

Citation:

Cite this paper

Online since:

September 2013

Authors:

Xi Hua Peng, Shan Xiong Chen, Xiao Yan Liu

Keywords:

Compressed Sensing, Conjugate Gradient, Matrix Factorization, Sparse Signals

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Candès, E. J. and Wakin, M. B. An Introduction to Compressive Sampling. [J]. Ieee Signal Proc Mag, 2008, 24(4) 118-121.

Google Scholar

[2] Candes, E. J. The restricted isometry property and its implications for compressed sensing [J]. Cr Math, 2008, 346(9-10), 589-592.

DOI: 10.1016/j.crma.2008.03.014

Google Scholar

[3] Donohoa, D. L. and Eladb, M. On the stability of the basis pursuit in the presence of noise [J]. Signal Process, 2006, 3, (86), 511–532.

Google Scholar

[4] Cai, T. T. and Wang, L. Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise [J]. Ieee T Inform Theory, 2011, 57(7), 4680-4688.

DOI: 10.1109/tit.2011.2146090

Google Scholar

[5] Zhang, T. Sparse Recovery With Orthogonal Matching Pursuit Under RIP. [J]. Ieee T Inform Theory, 2011, 57(9) , 6215-6221.

DOI: 10.1109/tit.2011.2162263

Google Scholar

[6] Needell, D. and Vershynin, R. Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. [J], Journal Found Computer Math, 2009, 9(3), 317–334.

DOI: 10.1007/s10208-008-9031-3

Google Scholar

[7] Cichocki, A., Cruces, S. and Amari, S. Generalized Alpha-Beta Divergences and Their Application to Robust Nonnegative Matrix Factorization. [J]. Entropy-Switz, 2011, 13(1) , 134-170.

DOI: 10.3390/e13010134

Google Scholar