Name
Title | ||
|---|---|---|
A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality. |
Abstract | ||
|---|---|---|
Symmetric nonnegative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection, and image segmentation. In this paper, we propose a novel nonconvex variable splitting method for solving SymNMF. The proposed algorithm is guaranteed to converge to the set of Karush–Kuhn–Tucker (KKT) points of the nonconvex SymNMF problem. Furthermore, it achieves a global sublinear convergence rate. We also show that the algorithm can be efficiently implemented in parallel. Further, sufficient conditions are provided that guarantee the global and local optimality of the obtained solutions. Extensive numerical results performed on both synthetic and real datasets suggest that the proposed algorithm converges quickly to a local minimum solution. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TSP.2017.2679687 | IEEE Trans. Signal Processing |
Keywords | DocType | Volume |
Signal processing algorithms,Convergence,Symmetric matrices,Algorithm design and analysis,Matrix decomposition,Linear programming,Clustering algorithms | Conference | 65 |
Issue | ISSN | Citations |
12 | 1053-587X | 7 |
PageRank | References | Authors |
0.48 | 36 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Songtao Lu | 1 | 84 | 19.52 |
| Mingyi Hong | 2 | 1533 | 91.29 |
| Zhengdao Wang | 3 | 1969 | 149.43 |