A least Squares Support Vector Machine Sparseness Algorithm

This paper proposes a method which uses density index function to sparse LS-SVM in high-dimensional feature space, gives a new method which takes each sample point as a clustering center to make hypersphere, so as to determine the fuzzy membership function in high-dimensional feature space, thus to establish a new fuzzy least squares support vector machine model, and the simulation experiment show that the model is effective and feasible.

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