Improved pruning algorithm using quadratic Renyi entropy for LS-SVM modeling

For the loss of sparseness in least squares support vector machine (LS-SVM) model, a new pruning algorithm using Renyi entropy for LS-SVM modeling is presented. The kernel principal component is adopted for data pre-processing, then the training subsets are divided randomly. To solve the problem that the conventional pruning algorithm cannot take full account the location of the Lagrange multiplier, the concept of quadratic Renyi entropy is introduced as the basis of training and pruning in LS-SVM modeling. The results of simulation verify the validity of the algorithms, thus the sparseness and generalization ability of the model can be improved. The presented algorithm can be applied to multiple-output modeling.