Recovery of sparse signal from an analog network model

Chi-Sing Leung; John Pui-Fai Sum; Ping-Man Lam; A. G. Constantinides

doi:10.1007/978-3-642-24965-5_42

Recovery of sparse signal from an analog network model

Chi-Sing Leung
, John Pui-Fai Sum
, Ping-Man Lam
, A. G. Constantinides

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

This paper presents an analog neural network model to recover sparse signals. In the original constrained optimization task for recovering sparse signals, the objective function is not differentiable. Hence, we recast the original nonlinear programming problem as a linear programming problem with linear inequality constraints and equality constraints. However, the second order gradient of the objective function is not convex at an equilibrium point. To solve this problem, we further modify the objective function such that the second order gradient is convex at the equilibrium point. This paper presents two sets of network dynamics. One is for the standard recovery of sparse signals. Another one is for the noisy situation. © 2011 Springer-Verlag.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	18th International Conference, ICONIP 2011, Proceedings
Publisher	Springer Verlag
Pages	373-380
Volume	7064 LNCS
ISBN (Print)	9783642249648
DOIs	https://doi.org/10.1007/978-3-642-24965-5_42
Publication status	Published - 2011
Event	18th International Conference on Neural Information Processing (ICONIP 2011) - Shanghai, China Duration: 13 Nov 2011 → 17 Nov 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7064 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Neural Information Processing (ICONIP 2011)
Place	China
City	Shanghai
Period	13/11/11 → 17/11/11

Research Keywords

Optimization
Sparse signal

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10.1007/978-3-642-24965-5_42

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Leung, C.-S., Sum, J. P.-F., Lam, P.-M., & Constantinides, A. G. (2011). Recovery of sparse signal from an analog network model. In Neural Information Processing: 18th International Conference, ICONIP 2011, Proceedings (Vol. 7064 LNCS, pp. 373-380). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7064 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-24965-5_42

Leung, Chi-Sing ; Sum, John Pui-Fai ; Lam, Ping-Man et al. / Recovery of sparse signal from an analog network model. Neural Information Processing: 18th International Conference, ICONIP 2011, Proceedings. Vol. 7064 LNCS Springer Verlag, 2011. pp. 373-380 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{a5480aa64a8046c09d7d74ce11fbe722,

title = "Recovery of sparse signal from an analog network model",

abstract = "This paper presents an analog neural network model to recover sparse signals. In the original constrained optimization task for recovering sparse signals, the objective function is not differentiable. Hence, we recast the original nonlinear programming problem as a linear programming problem with linear inequality constraints and equality constraints. However, the second order gradient of the objective function is not convex at an equilibrium point. To solve this problem, we further modify the objective function such that the second order gradient is convex at the equilibrium point. This paper presents two sets of network dynamics. One is for the standard recovery of sparse signals. Another one is for the noisy situation. {\textcopyright} 2011 Springer-Verlag.",

keywords = "Optimization, Sparse signal",

author = "Chi-Sing Leung and Sum, \{John Pui-Fai\} and Ping-Man Lam and Constantinides, \{A. G.\}",

year = "2011",

doi = "10.1007/978-3-642-24965-5\_42",

language = "English",

isbn = "9783642249648",

volume = "7064 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "373--380",

booktitle = "Neural Information Processing",

address = "Germany",

note = "18th International Conference on Neural Information Processing (ICONIP 2011) ; Conference date: 13-11-2011 Through 17-11-2011",

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Leung, C-S, Sum, JP-F, Lam, P-M & Constantinides, AG 2011, Recovery of sparse signal from an analog network model. in Neural Information Processing: 18th International Conference, ICONIP 2011, Proceedings. vol. 7064 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7064 LNCS, Springer Verlag, pp. 373-380, 18th International Conference on Neural Information Processing (ICONIP 2011), Shanghai, China, 13/11/11. https://doi.org/10.1007/978-3-642-24965-5_42

Recovery of sparse signal from an analog network model. / Leung, Chi-Sing; Sum, John Pui-Fai; Lam, Ping-Man et al.
Neural Information Processing: 18th International Conference, ICONIP 2011, Proceedings. Vol. 7064 LNCS Springer Verlag, 2011. p. 373-380 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7064 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Recovery of sparse signal from an analog network model

AU - Leung, Chi-Sing

AU - Sum, John Pui-Fai

AU - Lam, Ping-Man

AU - Constantinides, A. G.

PY - 2011

Y1 - 2011

N2 - This paper presents an analog neural network model to recover sparse signals. In the original constrained optimization task for recovering sparse signals, the objective function is not differentiable. Hence, we recast the original nonlinear programming problem as a linear programming problem with linear inequality constraints and equality constraints. However, the second order gradient of the objective function is not convex at an equilibrium point. To solve this problem, we further modify the objective function such that the second order gradient is convex at the equilibrium point. This paper presents two sets of network dynamics. One is for the standard recovery of sparse signals. Another one is for the noisy situation. © 2011 Springer-Verlag.

AB - This paper presents an analog neural network model to recover sparse signals. In the original constrained optimization task for recovering sparse signals, the objective function is not differentiable. Hence, we recast the original nonlinear programming problem as a linear programming problem with linear inequality constraints and equality constraints. However, the second order gradient of the objective function is not convex at an equilibrium point. To solve this problem, we further modify the objective function such that the second order gradient is convex at the equilibrium point. This paper presents two sets of network dynamics. One is for the standard recovery of sparse signals. Another one is for the noisy situation. © 2011 Springer-Verlag.

KW - Optimization

KW - Sparse signal

UR - https://www.scopus.com/pages/publications/81855218196

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-81855218196&origin=recordpage

U2 - 10.1007/978-3-642-24965-5_42

DO - 10.1007/978-3-642-24965-5_42

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783642249648

VL - 7064 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 373

EP - 380

BT - Neural Information Processing

PB - Springer Verlag

T2 - 18th International Conference on Neural Information Processing (ICONIP 2011)

Y2 - 13 November 2011 through 17 November 2011

ER -

Leung CS, Sum JPF, Lam PM, Constantinides AG. Recovery of sparse signal from an analog network model. In Neural Information Processing: 18th International Conference, ICONIP 2011, Proceedings. Vol. 7064 LNCS. Springer Verlag. 2011. p. 373-380. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-24965-5_42

Recovery of sparse signal from an analog network model

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