Name
Abstract | ||
|---|---|---|
This paper introduces a matrix cofactorization approach to perform spectral unmixing and classification jointly. After formulating the unmixing and classification tasks as matrix factorization problems, a link is introduced between the two coding matrices, namely the abundance matrix and the feature matrix. This coupling term can be interpreted as a clustering term where the abundance vectors are clustered and the resulting attribution vectors are then used as feature vectors. The overall non-smooth, non-convex optimization problem is solved using a proximal alternating linearized minimization algorithm (PALM) ensuring convergence to a critical point. The quality of the obtained results is finally assessed by comparison to other conventional algorithms on semi-synthetic yet realistic dataset. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/EUSIPCO.2019.8903037 | 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) |
Keywords | Field | DocType |
supervised learning, spectral unmixing, cofactorization, hyperspectral images | Convergence (routing),Feature vector,Pattern recognition,Matrix (mathematics),Computer science,Matrix decomposition,Hyperspectral imaging,Supervised learning,Artificial intelligence,Cluster analysis,Optimization problem | Conference |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrien Lagrange | 1 | 13 | 2.63 |
| Mathieu Fauvel | 2 | 742 | 42.30 |
| Stéphane May | 3 | 4 | 4.15 |
| José M. Bioucas-Dias | 4 | 0 | 0.34 |
| Nicolas Dobigeon | 5 | 2070 | 108.02 |