Name
Abstract | ||
|---|---|---|
Nonnegative matrix factorization (NMF) is an emerging tool for meaningful low-rank matrix representation. In NMF, explicit constraints are usually required, such that NMF generates desired products (or factorizations), especially when the products have significant sparseness features. It is known that the ability of NMF in learning sparse representation can be improved by embedding a smoothness fa... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TNNLS.2016.2517096 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Matrix decomposition,Sparse matrices,Feature extraction,Standards,Adaptation models,Linear programming,Face | Interpretability,Embedding,Pattern recognition,Computer science,Matrix decomposition,Sparse approximation,Artificial intelligence,Linear programming,Non-negative matrix factorization,Machine learning,Matrix representation,Sparse matrix | Journal |
Volume | Issue | ISSN |
28 | 4 | 2162-237X |
Citations | PageRank | References |
19 | 0.74 | 29 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zu-yuan Yang | 1 | 312 | 24.12 |
| Yong Xiang | 2 | 1137 | 93.92 |
| Kan Xie | 3 | 351 | 28.49 |
| Yue Lai | 4 | 24 | 4.02 |