Dictionary learning from sparsely corrupted or compressed signals

Christoph Studer; Richard G. Baraniuk

doi:10.1109/ICASSP.2012.6288631

Dictionary learning from sparsely corrupted or compressed signals

Christoph Studer, Richard G. Baraniuk

Houston Methodist

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

33 Scopus citations

Abstract

In this paper, we investigate dictionary learning (DL) from sparsely corrupted or compressed signals. We consider three cases: I) the training signals are corrupted, and the locations of the corruptions are known, II) the locations of the sparse corruptions are unknown, and III) DL from compressed measurements, as it occurs in blind compressive sensing. We develop two efficient DL algorithms that are capable of learning dictionaries from sparsely corrupted or compressed measurements. Empirical phase transitions and an in-painting example demonstrate the capabilities of our algorithms.

Original language	English (US)
Title of host publication	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages	3341-3344
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2012.6288631
State	Published - 2012
Event	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan Duration: Mar 25 2012 → Mar 30 2012

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Other

Other	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/Territory	Japan
City	Kyoto
Period	3/25/12 → 3/30/12

Keywords

compressive sensing
Dictionary learning
in-painting
signal restoration
sparse approximation

ASJC Scopus subject areas

Signal Processing
Software
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2012.6288631

Cite this

Studer, C., & Baraniuk, R. G. (2012). Dictionary learning from sparsely corrupted or compressed signals. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 3341-3344). Article 6288631 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6288631

Dictionary learning from sparsely corrupted or compressed signals. / Studer, Christoph; Baraniuk, Richard G.
2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. p. 3341-3344 6288631 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Studer, C & Baraniuk, RG 2012, Dictionary learning from sparsely corrupted or compressed signals. in 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings., 6288631, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 3341-3344, 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012, Kyoto, Japan, 3/25/12. https://doi.org/10.1109/ICASSP.2012.6288631

@inproceedings{86f4941b17c14feaacefff16cfd1d0cb,

title = "Dictionary learning from sparsely corrupted or compressed signals",

abstract = "In this paper, we investigate dictionary learning (DL) from sparsely corrupted or compressed signals. We consider three cases: I) the training signals are corrupted, and the locations of the corruptions are known, II) the locations of the sparse corruptions are unknown, and III) DL from compressed measurements, as it occurs in blind compressive sensing. We develop two efficient DL algorithms that are capable of learning dictionaries from sparsely corrupted or compressed measurements. Empirical phase transitions and an in-painting example demonstrate the capabilities of our algorithms.",

keywords = "compressive sensing, Dictionary learning, in-painting, signal restoration, sparse approximation",

author = "Christoph Studer and Baraniuk, {Richard G.}",

year = "2012",

doi = "10.1109/ICASSP.2012.6288631",

language = "English (US)",

isbn = "9781467300469",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

pages = "3341--3344",

booktitle = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings",

note = "2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 ; Conference date: 25-03-2012 Through 30-03-2012",

}

TY - GEN

T1 - Dictionary learning from sparsely corrupted or compressed signals

AU - Studer, Christoph

AU - Baraniuk, Richard G.

PY - 2012

Y1 - 2012

N2 - In this paper, we investigate dictionary learning (DL) from sparsely corrupted or compressed signals. We consider three cases: I) the training signals are corrupted, and the locations of the corruptions are known, II) the locations of the sparse corruptions are unknown, and III) DL from compressed measurements, as it occurs in blind compressive sensing. We develop two efficient DL algorithms that are capable of learning dictionaries from sparsely corrupted or compressed measurements. Empirical phase transitions and an in-painting example demonstrate the capabilities of our algorithms.

AB - In this paper, we investigate dictionary learning (DL) from sparsely corrupted or compressed signals. We consider three cases: I) the training signals are corrupted, and the locations of the corruptions are known, II) the locations of the sparse corruptions are unknown, and III) DL from compressed measurements, as it occurs in blind compressive sensing. We develop two efficient DL algorithms that are capable of learning dictionaries from sparsely corrupted or compressed measurements. Empirical phase transitions and an in-painting example demonstrate the capabilities of our algorithms.

KW - compressive sensing

KW - Dictionary learning

KW - in-painting

KW - signal restoration

KW - sparse approximation

UR - http://www.scopus.com/inward/record.url?scp=84867618670&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84867618670&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2012.6288631

DO - 10.1109/ICASSP.2012.6288631

M3 - Conference contribution

AN - SCOPUS:84867618670

SN - 9781467300469

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 3341

EP - 3344

BT - 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings

T2 - 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012

Y2 - 25 March 2012 through 30 March 2012

ER -

Dictionary learning from sparsely corrupted or compressed signals

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this