Journal
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
Volume 5, Issue 3, Pages 459-468Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s13042-013-0183-3
Keywords
Semi-supervised classification; Manifold regularization; Twin support vector machine; Smooth technique
Categories
Funding
- National Natural Science Foundation of China [11201426, 61203133, 10971223, 11071252]
- Zhejiang Provincial Natural Science Foundation of China [LQ12A01020, LQ13F030010]
- Science and Technology Foundation of Department of Education of Zhejiang Province [Y201225179]
Ask authors/readers for more resources
Laplacian twin support vector machine (Lap-TSVM) is a state-of-the-art nonparallel-planes semi-supervised classifier. It tries to exploit the geometrical information embedded in unlabeled data to boost its generalization ability. However, Lap-TSVM may endure heavy burden in training procedure since it needs to solve two quadratic programming problems (QPPs) with the matrix inversion'' operation. In order to enhance the performance of Lap-TSVM, this paper presents a new formulation of Lap-TSVM, termed as Lap-STSVM. Rather than solving two QPPs in dual space, firstly, we convert the primal constrained QPPs of Lap-TSVM into unconstrained minimization problems (UMPs). Afterwards, a smooth technique is introduced to make these UMPs twice differentiable. At last, a fast Newton-Armijo algorithm is designed to solve the UMPs in Lap-STSVM. Experimental evaluation on both artificial and real-world datasets demonstrate the benefits of the proposed approach.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available