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A P-ADMM for sparse quadratic kernel-free least squares semi-supervised support vector machine
- Source :
- Neurocomputing. 306:37-50
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
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.
- Subjects :
- 021103 operations research
Cognitive Neuroscience
0211 other engineering and technologies
02 engineering and technology
Regularization (mathematics)
Least squares
Computer Science Applications
Support vector machine
Quadratic equation
Rate of convergence
Artificial Intelligence
Kernel (statistics)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Quadratic programming
Algorithm
Smoothing
Mathematics
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 306
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........236a7bdd174a35c2a6bed9c68b97cfb6