A New Features Selection model: Least Squares Support Vector Machine with Mixture of Kernel
Liwei Wei, Wenwu Li, Qiang Xiao · 2015
In this paper, a least squares support vector machine with mixture kernel (LS-SVM-MK) is proposed to solve the problem of the traditional LS-SVM model, such as the loss of sparseness and robustness.Thus that will result in slow testing speed and poor generalization performance.The revision model LS-SVM-MK is equivalent to solve a linear equation set with deficient rank just like the over complete problem in independent component analysis.A minimum of 1-penalty based object function is chosen to get the sparse and robust solution.Some UCI datasets are used to demonstrate the effectiveness of this model.The experimental results show that LS-SVM-MK can obtain a small number of features and improve the generalization ability of LS-SVM.