Name
Abstract | ||
|---|---|---|
This paper contributes to the solution of the non-negative least squares problem (NNLS). The NNLS problem constitutes a substantial part of many computer vision methods and methods in other fields, too. We propose a novel sequential coordinate-wise algorithm which is easy to implement and it is able to cope with large scale problems. We also derive stopping conditions which allow to control the distance of the solution found to the optimal one in terms of the optimized objective function. The proposed algorithm showed promising performance in comparison to the projected Landweber method. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11556121_50 | CAIP |
Keywords | Field | DocType |
computer vision method,optimized objective function,promising performance,substantial part,nnls problem,squares problem,projected landweber method,large scale problem,proposed algorithm,novel sequential coordinate-wise algorithm,objective function | Least squares,Non-negative least squares,Pattern recognition,Iterative method,Computer science,Pattern analysis,Algorithm,Image processing,Artificial intelligence | Conference |
Volume | ISSN | ISBN |
3691 | 0302-9743 | 3-540-28969-0 |
Citations | PageRank | References |
19 | 1.23 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vojtěch Franc | 1 | 584 | 55.78 |
| Václav Hlaváč | 2 | 216 | 13.42 |
| mirko navara | 3 | 367 | 46.14 |