Laplacian generalized elastic net Lp-norm nonparallel support vector machine for semi-supervised classification.

For semi-supervised learning, a few labeled data and a large number of unlabeled data are used to construct a reasonable classifier. In recent years, many semi-supervised learning methods have been proposed and achieved good performance, especially for the graph-based approaches that can exploit the geometric information embedded in the data. Motivated by the success of generalized elastic net Lp-norm nonparallel support vector machine (GLpNPSVM) and the graph-based regularization term, in this paper, a novel Laplacian generalized elastic net Lp-norm nonparallel support vector machine for semi-supervised learning (Lap-GLpNPSVM) is proposed. A Lp-norm graph regularization term is introduced to improve the performance by the adjustability of the value of p. Experimental results on two synthetic datasets, fifteen UCI datasets, and Handwritten Numeral datasets demonstrate that proposed methods outperform other state-of-the-art methods. [ABSTRACT FROM AUTHOR]