Hybrid Wavelet Model Construction Using Orthogonal Forward Selection with Boosting Search

Abstract

This paper considers sparse regression modeling using a generalized kernel model in which each kernel regressor has its individually tuned center vector and diagonal covariance matrix. An orthogonal least squares forward selection procedure is employed to select the regressors one by one using a guided random search algorithm. In order to prevent the possible over-fitting, a practical method to select termination threshold is used. A novel hybrid wavelet is constructed to make the model sparser. The experimental results show that this generalized model outperforms traditional methods in terms of precision and sparseness. And the models with wavelet and hybrid kernel have a much faster convergence rate as compared to that with conventional RBF kernel.