Non-negative matrix factorization with sparseness constraints
| dc.creator | Hoyer, Patrik O. | |
| dc.date | 2004-08-25 | |
| dc.date.accessioned | 2026-07-07T03:21:43Z | |
| dc.date.available | 2026-07-07T03:21:43Z | |
| dc.description | Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how explicitly incorporating the notion of `sparseness' improves the found decompositions. Additionally, we provide complete MATLAB code both for standard NMF and for our extension. Our hope is that this will further the application of these methods to solving novel data-analysis problems. | |
| dc.identifier | https://arxiv.org/abs/cs/0408058 | |
| dc.identifier | http://arxiv.org/abs/cs/0408058 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/32310 | |
| dc.subject | Machine Learning | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.title | Non-negative matrix factorization with sparseness constraints | |
| dc.type | text |