For lack of sparseness characteristic in support vector machines(SVM)by least squares solution(LSSVM)

a density weighted pruning algorithm to improve the sparseness of the LSSVM regression model is investigated. The estimation error of training sample is weighted by corresponding density value and the potential contribution of training sample is obtained. Then the sample with the greatest value in the sorted spectrum of the potential contribution is selected as the support vector. The density and the potential contribution of training sample are updated and the potential contribution of the neighborhood sample of support vectors is significantly reduced. Thus the rechoosing of similar sample as support vector is properly avoided. More support vectors in the training sample set are iteratively selected

until the user-defined performance is achieved

thus the sparse LSSVM model is obtained. The simulation and practical applications indicate that the proposed method performs more effectively than Suykens standard sparse method for removing the redundant support vector with better sparseness and robustness.