Tampere University of Technology

TUTCRIS Research Portal

Cosparse dictionary learning for the orthogonal case

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Standard

Cosparse dictionary learning for the orthogonal case. / Irofti, Paul; Dumitrescu, Bogdan.

2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015 - Joint Conference SINTES 19, SACCS 15, SIMSIS 19. IEEE, 2015. p. 343-347.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Irofti, P & Dumitrescu, B 2015, Cosparse dictionary learning for the orthogonal case. in 2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015 - Joint Conference SINTES 19, SACCS 15, SIMSIS 19. IEEE, pp. 343-347, International conference on system theory, control and computing, 1/01/14. https://doi.org/10.1109/ICSTCC.2015.7321317

APA

Irofti, P., & Dumitrescu, B. (2015). Cosparse dictionary learning for the orthogonal case. In 2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015 - Joint Conference SINTES 19, SACCS 15, SIMSIS 19 (pp. 343-347). IEEE. https://doi.org/10.1109/ICSTCC.2015.7321317

Vancouver

Irofti P, Dumitrescu B. Cosparse dictionary learning for the orthogonal case. In 2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015 - Joint Conference SINTES 19, SACCS 15, SIMSIS 19. IEEE. 2015. p. 343-347 https://doi.org/10.1109/ICSTCC.2015.7321317

Author

Irofti, Paul ; Dumitrescu, Bogdan. / Cosparse dictionary learning for the orthogonal case. 2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015 - Joint Conference SINTES 19, SACCS 15, SIMSIS 19. IEEE, 2015. pp. 343-347

Bibtex - Download

@inproceedings{1b252bbbe47943d9afd5927fae788dc2,
title = "Cosparse dictionary learning for the orthogonal case",
abstract = "Dictionary learning is usually approached by looking at the support of the sparse representations. Recent years have shown results in dictionary improvement by investigating the cosupport via the analysis-based cosparse model. In this paper we present a new cosparse learning algorithm for orthogonal dictionary blocks that provides significant dictionary recovery improvements and representation error shrinkage. Furthermore, we show the beneficial effects of using this algorithm inside existing methods based on building the dictionary as a structured union of orthonormal bases.",
keywords = "cosparse, dictionary design, orthogonal blocks, sparse representation",
author = "Paul Irofti and Bogdan Dumitrescu",
year = "2015",
month = "11",
day = "5",
doi = "10.1109/ICSTCC.2015.7321317",
language = "English",
isbn = "9781479984817",
pages = "343--347",
booktitle = "2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015 - Joint Conference SINTES 19, SACCS 15, SIMSIS 19",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Cosparse dictionary learning for the orthogonal case

AU - Irofti, Paul

AU - Dumitrescu, Bogdan

PY - 2015/11/5

Y1 - 2015/11/5

N2 - Dictionary learning is usually approached by looking at the support of the sparse representations. Recent years have shown results in dictionary improvement by investigating the cosupport via the analysis-based cosparse model. In this paper we present a new cosparse learning algorithm for orthogonal dictionary blocks that provides significant dictionary recovery improvements and representation error shrinkage. Furthermore, we show the beneficial effects of using this algorithm inside existing methods based on building the dictionary as a structured union of orthonormal bases.

AB - Dictionary learning is usually approached by looking at the support of the sparse representations. Recent years have shown results in dictionary improvement by investigating the cosupport via the analysis-based cosparse model. In this paper we present a new cosparse learning algorithm for orthogonal dictionary blocks that provides significant dictionary recovery improvements and representation error shrinkage. Furthermore, we show the beneficial effects of using this algorithm inside existing methods based on building the dictionary as a structured union of orthonormal bases.

KW - cosparse

KW - dictionary design

KW - orthogonal blocks

KW - sparse representation

U2 - 10.1109/ICSTCC.2015.7321317

DO - 10.1109/ICSTCC.2015.7321317

M3 - Conference contribution

SN - 9781479984817

SP - 343

EP - 347

BT - 2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015 - Joint Conference SINTES 19, SACCS 15, SIMSIS 19

PB - IEEE

ER -