Name
Title | ||
|---|---|---|
A Fast Non-Smooth Nonnegative Matrix Factorization for Learning Sparse Representation. |
Abstract | ||
|---|---|---|
Nonnegative matrix factorization (NMF) is a hot topic in machine learning and data processing. Recently, a constrained version, non-smooth NMF (NsNMF), shows a great potential in learning meaningful sparse representation of the observed data. However, it suffers from a slow linear convergence rate, discouraging its applications to large-scale data representation. In this paper, a fast NsNMF (FNsNMF) algorithm is proposed to speed up NsNMF. In the proposed method, it first shows that the cost function of the derived sub-problem is convex and the corresponding gradient is Lipschitz continuous. Then, the optimization to this function is replaced by solving a proximal function, which is designed based on the Lipschitz constant and can be solved through utilizing a constructed fast convergent sequence. Due to the usage of the proximal function and its efficient optimization, our method can achieve a nonlinear convergence rate, much faster than NsNMF. Simulations in both computer generated data and the real-world data show the advantages of our algorithm over the compared methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ACCESS.2016.2605704 | IEEE ACCESS |
Keywords | Field | DocType |
Nonnegative matrix factorization,sparse representation,nonlinear convergence rate | Mathematical optimization,External Data Representation,Computer science,Sparse approximation,Matrix decomposition,Algorithm,Non-negative matrix factorization,Rate of convergence,Lipschitz continuity,Cuthill–McKee algorithm,Sparse matrix,Distributed computing | Journal |
Volume | ISSN | Citations |
4 | 2169-3536 | 4 |
PageRank | References | Authors |
0.42 | 20 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zu-yuan Yang | 1 | 312 | 24.12 |
| Yu Zhang | 2 | 40 | 10.46 |
| Wei Yan | 3 | 38 | 23.56 |
| Yong Xiang | 4 | 1137 | 93.92 |
| Shengli Xie | 5 | 2530 | 161.51 |