Name
Abstract | ||
|---|---|---|
Nonnegative matrix factorization (NMF), which aims at obtaining the nonnegative low-dimensional representation of data, has received wide attention. To obtain more effective nonnegative discriminant bases from the original NMF, in this paper, a novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification. NDMF integrates the nonnegative constraint, ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TCSVT.2016.2539779 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | Field | DocType |
Matrix decomposition,Linear programming,Image reconstruction,Euclidean distance,Principal component analysis,Image classification,Convergence | Pattern recognition,Subspace topology,Discriminant,Matrix (mathematics),Euclidean distance,Matrix decomposition,Orthogonality,Artificial intelligence,Non-negative matrix factorization,Principal component analysis,Mathematics | Journal |
Volume | Issue | ISSN |
27 | 7 | 1051-8215 |
Citations | PageRank | References |
20 | 0.60 | 24 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuwu Lu | 1 | 196 | 12.50 |
| Zhihui Lai | 2 | 1204 | 76.03 |
| Yong Xu | 3 | 339 | 31.64 |
| Xuelong Li | 4 | 15049 | 617.31 |
| David Zhang | 5 | 2337 | 102.40 |
| Yuan Chun | 6 | 265 | 32.08 |