Name
Abstract | ||
|---|---|---|
Nonnegative matrix factorization (NMF) is a very attractive scheme in learning data representation, and constrained NMF further improves its ability. In this paper, we focus on the L2-norm constraint due to its wide applications in face recognition, hyperspectral unmixing, and so on. A new algorithm of NMF with fixed L2-norm constraint is proposed by using the Lagrange multiplier scheme. In our method, we derive the involved Lagrange multiplier and learning rate which are hard to tune. As a result, our method can preserve the constraint exactly during the iteration. Simulations in both computer-generated data and real-world data show the performance of our algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00034-018-1012-4 | Circuits, Systems, and Signal Processing |
Keywords | Field | DocType |
Nonnegative matrix factorization,Multiplicative updates,L2-norm,Constrained NMF | Facial recognition system,Mathematical optimization,External Data Representation,Lagrange multiplier,Algorithm,Hyperspectral imaging,Non-negative matrix factorization,Norm (mathematics),Mathematics | Journal |
Volume | Issue | ISSN |
38 | 7 | 1531-5878 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zu-yuan Yang | 1 | 312 | 24.12 |
| Yifei Hu | 2 | 0 | 0.34 |
| Naiyao Liang | 3 | 21 | 2.59 |
| Jun Lv | 4 | 0 | 0.34 |