| dc.creator | Hosseini | |
| dc.creator | Shahram; Deville | |
| dc.creator | Yannick; Duarte | |
| dc.creator | Leonardo T.; Selloum | |
| dc.creator | Ahmed | |
| dc.date | 2016 | |
| dc.date | 2017-11-13T13:44:44Z | |
| dc.date | 2017-11-13T13:44:44Z | |
| dc.date.accessioned | 2018-03-29T05:59:24Z | |
| dc.date.available | 2018-03-29T05:59:24Z | |
| dc.identifier | 978-1-5090-0746-2 | |
| dc.identifier | 2016 Ieee 26th International Workshop On Machine Learning For Signal Processing (mlsp). Ieee, p. , 2016. | |
| dc.identifier | 2161-0363 | |
| dc.identifier | WOS:000392177200082 | |
| dc.identifier | http://ieeexplore.ieee.org/document/7738890/ | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/328848 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1365873 | |
| dc.description | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
| dc.description | This paper proposes a new constrained method, based on non-negative matrix factorization, for blindly separating linear-quadratic (LQ) mixtures of mutually uncorrelated source signals when the sources and mixing parameters are all non-negative. The uncorrelatedness of the sources is used as a regularization term in the cost function. The main advantage of exploiting uncorrelatedness in this manner is that the inversion of the mixing model, which is a difficult task in the case of determined LQ mixtures, is not required, contrary to the classical LQ methods based on independent component analysis. Experimental results using artificial data and real-world chemical data confirm the effectiveness of our method. | |
| dc.description | National Council for Scientific | |
| dc.description | Technological Development (CNPq, Brazil) | |
| dc.description | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
| dc.description | 26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP) | |
| dc.description | SEP 13-16, 2016 | |
| dc.description | Salerno, ITALY | |
| dc.description | | |
| dc.language | English | |
| dc.publisher | IEEE | |
| dc.publisher | New York | |
| dc.relation | 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) | |
| dc.rights | fechado | |
| dc.source | WOS | |
| dc.subject | Blind Source Separation | |
| dc.subject | Non-negative Matrix Factorization (nmf) | |
| dc.subject | Linear-quadratic Mixtures | |
| dc.subject | Un-correlated Sources | |
| dc.subject | Regularization | |
| dc.title | Extending Nmf To Blindly Separate Linear-quadratic Mixtures Of Uncorrelated Sources | |
| dc.type | Actas de congresos | |
