caa a22 4500 606160434 CHVBK 20210128100630.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00521-014-1728-4 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1728-4 Manifold proximal support vector machine with mixed-norm for semi-supervised classification [Elektronische Daten] [Zhiqiang Zhang, Ling Zhen, Naiyang Deng, Junyan Tan] Since labeling all the samples by the user is time-consuming and fastidious, we often obtain a large amount of unlabeled examples and only a small number of labeled examples in classification. In this context, the classification is called semi-supervised learning. In this paper, we propose a novel semi-supervised learning methodology, named Laplacian mixed-norm proximal support vector machine Lap-MNPSVM for short. In the optimization problem of Lap-MNPSVM, the information from the unlabeled examples is used in a form of Laplace regularization, and $$l_p$$ l p norm ( $$0\,<\,p\,<\,1$$ 0 < p < 1 ) regularizer is introduced to standard proximal support vector machine to control sparsity and the feature selection. To solve the nonconvex optimization problem in Lap-MNPSVM, an efficient algorithm is proposed by solving a series systems of linear equations, and the lower bounds of the solution are established, which are extremely helpful for feature selection. Experiments carried out on synthetic datasets and the real-world datasets show the feasibility and effectiveness of the proposed method. The Natural Computing Applications Forum, 2014 Semi-supervised classification nationallicence Manifold regularization nationallicence Mixed-norm nationallicence Sparsity nationallicence Proximal support vector machine nationallicence Zhang Zhiqiang School of Mechanical Engineering, Beijing Institute of Technology, 100081, Beijing, China aut Zhen Ling College of Science, China Agricultural University, 100083, Beijing, China aut Deng Naiyang College of Science, China Agricultural University, 100083, Beijing, China aut Tan Junyan College of Science, China Agricultural University, 100083, Beijing, China aut Neural Computing and Applications Springer London 26/2(2015-02-01), 399-407 0941-0643 26:2<399 2015 26 521 https://doi.org/10.1007/s00521-014-1728-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1728-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhang Zhiqiang School of Mechanical Engineering, Beijing Institute of Technology, 100081, Beijing, China aut NATIONALLICENCE 700 1- Zhen Ling College of Science, China Agricultural University, 100083, Beijing, China aut NATIONALLICENCE 700 1- Deng Naiyang College of Science, China Agricultural University, 100083, Beijing, China aut NATIONALLICENCE 700 1- Tan Junyan College of Science, China Agricultural University, 100083, Beijing, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/2(2015-02-01), 399-407 0941-0643 26:2<399 2015 26 521