Name
Abstract | ||
|---|---|---|
Nonnegative matrix factorization (NMF), known as a famous matrix factorization technique, has been widely used in pattern recognition and computer vision. NMF represents the input data matrix as a product of two nonnegative factors. As NMF is based on the Euclidean distance, which is sensitive to noise or errors in the data, some robust NMF methods are proposed. Mainly focusing on parts-based repr... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCSVT.2017.2749980 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | Field | DocType |
Robustness,Sparse matrices,Matrix decomposition,Image classification,Manifolds,Euclidean distance,Geometry | Dimensionality reduction,Pattern recognition,Computer science,Matrix (mathematics),Matrix decomposition,Robustness (computer science),Factorization,Non-negative matrix factorization,Artificial intelligence,Contextual image classification,Sparse matrix | Journal |
Volume | Issue | ISSN |
28 | 12 | 1051-8215 |
Citations | PageRank | References |
5 | 0.40 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuwu Lu | 1 | 196 | 12.50 |
| Zhihui Lai | 2 | 1204 | 76.03 |
| Xuelong Li | 3 | 15049 | 617.31 |
| David Zhang | 4 | 2337 | 102.40 |
| W. K. Wong | 5 | 957 | 49.71 |
| Yuan Chun | 6 | 265 | 32.08 |