| dc.contributor.author | Hocke, Jens | |
| dc.contributor.author | Labusch, Kai | |
| dc.contributor.author | Barth, Erhardt | |
| dc.contributor.author | Martinetz, Thomas | |
| dc.date | 2012-11-01 | |
| dc.date.accessioned | 2018-01-08T09:16:10Z | |
| dc.date.available | 2018-01-08T09:16:10Z | |
| dc.date.issued | 2012 | |
| dc.identifier.issn | 1610-1987 | |
| dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/11313 | |
| dc.description.abstract | Sparse 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.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 26, No. 4 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Blind source separation | |
| dc.subject | Digit recognition | |
| dc.subject | Image deconvolution | |
| dc.subject | Image reconstruction | |
| dc.subject | K-SVD | |
| dc.subject | Neural Gas | |
| dc.subject | Sparse Coding | |
| dc.title | Sparse Coding and Selected Applications | |
| dc.type | Text/Journal Article | |
| mci.reference.pages | 349-355 | |
| gi.identifier.doi | 10.1007/s13218-012-0197-0 | |
