Ling Zhen

Adaptive feature selection via a new version of support vector machine

This paper focuses on feature selection in classification. A new version of support vector machine (SVM) named p-norm support vector machine (

Support vector machine with hypergraph-based pairwise constraints.

p\in[0,1]

Manifold proximal support vector machine with mixed-norm for semi-supervised classification

) is proposed. Different from the standard SVM, the p-norm

Semi-supervised dimension reduction based on hypergraph embedding for hyperspectral images

(p\in[0,1])

A novel projection nonparallel support vector machine for pattern classification

of the normal vector of the decision plane is used which leads to more sparse solution. Our new model can not only select less features but also improve the classification accuracy by adjusting the parameter p. The numerical experiments results show that our p-norm SVM is more effective than some usual methods in feature selection.

A NEW SUPPORT VECTOR MACHINE FOR THE CLASSIFICATION OF POSITIVE AND UNLABELED EXAMPLES

By promoting the parallel hyperplanes to non-parallel ones in SVM, twin support vector machines (TWSVM) have attracted more attention. There are many modifications of them. However, most of the modifications minimize the loss function subject to the I2-norm or I1-norm penalty. These methods are non-adaptive since their penalty forms are fixed and pre-determined for any types of data. To overcome the above shortcoming, we propose lp norm least square twin support vector machine (lpLSTSVM). Our new model is an adaptive learning procedure with lp-norm (0<p<1), where p is viewed as an adjustable parameter and can be automatically chosen by data. By adjusting the parameter p, lpLSTSVM can not only select relevant features but also improve the classification accuracy. The solutions of the optimization problems in lpLSTSVM are obtained by solving a series systems of linear equations (LEs) and the lower bounds of the solution is established which is extremely helpful for feature selection. Experiments carried out on several standard UCI data sets and synthetic data sets show the feasibility and effectiveness of the proposed method.