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

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

29 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 - Oct 23 2012
Event	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan Duration: Mar 25 2012 → Mar 30 2012

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

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10.1109/ICASSP.2012.6288631

Cite this

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, 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

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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.",

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Dictionary learning from sparsely corrupted or compressed signals

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Keywords

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