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Sparse Coding and Selected Applications

Kurzanzeige
dc.contributor.authorHocke, Jens
dc.contributor.authorLabusch, Kai
dc.contributor.authorBarth, Erhardt
dc.contributor.authorMartinetz, Thomas
dc.date.accessioned2018-01-08T09:16:10Z
dc.date.available2018-01-08T09:16:10Z
dc.date.issued2012
dc.description.abstractSparse coding has become a widely used framework in signal processing and pattern recognition. After a motivation of the principle of sparse coding we show the relation to Vector Quantization and Neural Gas and describe how this relation can be used to generalize Neural Gas to successfully learn sparse coding dictionaries. We explore applications of sparse coding to image-feature extraction, image reconstruction and deconvolution, and blind source separation.
dc.identifier.pissn1610-1987
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/11313
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 26, No. 4
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectBlind source separation
dc.subjectDigit recognition
dc.subjectImage deconvolution
dc.subjectImage reconstruction
dc.subjectK-SVD
dc.subjectNeural Gas
dc.subjectSparse Coding
dc.titleSparse Coding and Selected Applications
dc.typeText/Journal Article
gi.citation.endPage355
gi.citation.startPage349

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Künstliche Intelligenz 26(4) - November 2012