Name
Title | ||
|---|---|---|
A P-ADMM for sparse quadratic kernel-free least squares semi-supervised support vector machine. |
Abstract | ||
|---|---|---|
In this paper, we propose a sparse quadratic kernel-free least squares semi-supervised support vector machine model by adding an L1 norm regularization term to the objective function and using the least squares method, which results in a nonconvex and nonsmooth quadratic programming problem. For computational considerations, we use the smoothing technique and consensus technique. Then we adopt the proximal alternating direction method of multipliers (P-ADMM) to solve it, as well as propose a strategy of parameter selection. Then we not only derive the convergence analysis of algorithm, but also estimate the convergence rate as o(1/k), where k is the number of iteration. This gives the best bound of P-ADMM known so far for nonconvex consensus problem. To demonstrate the efficiency of our model, we compare the proposed method with several state-of-the-art methods. The numerical results show that our model can achieve both better accuracy and sparsity. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2018.03.069 | Neurocomputing |
Keywords | Field | DocType |
Semi-supervised support vector machine,Sparsity,Quadratic kernel-free least square,Smoothing,Consensus model,P-ADMM | Kernel (linear algebra),Least squares,Pattern recognition,Support vector machine,Quadratic equation,Algorithm,Smoothing,Regularization (mathematics),Artificial intelligence,Rate of convergence,Quadratic programming,Mathematics | Journal |
Volume | ISSN | Citations |
306 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaru Zhan | 1 | 0 | 0.34 |
| Yanqin Bai | 2 | 4 | 2.43 |
| Wei Zhang | 3 | 0 | 0.68 |
| Shihui Ying | 4 | 233 | 23.32 |