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Cosparse dictionary learning for the orthogonal case

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

Details

Original languageEnglish
Title of host publication2015 19th International Conference on System Theory, Control and Computing, ICSTCC 2015 - Joint Conference SINTES 19, SACCS 15, SIMSIS 19
PublisherIEEE
Pages343-347
Number of pages5
ISBN (Print)9781479984817
DOIs
Publication statusPublished - 5 Nov 2015
Publication typeA4 Article in a conference publication
EventInternational conference on system theory, control and computing -
Duration: 1 Jan 2014 → …

Conference

ConferenceInternational conference on system theory, control and computing
Period1/01/14 → …

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

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