Abstract
This paper considers sparse regression modelling using a generalised kernel model in which each kernel regressor has its individually tuned centre vector and diagonal covariance matrix. An Orthogonal Least Squares (OLS) forward selection procedure is employed to select the regressors one by one using a guided random search algorithm. In order to prevent the possible overfitting, a practical method to select the termination threshold is used. A novel hybrid wavelet is constructed to make the model sparser. The experimental results show that this generalised model outperforms the traditional methods in terms of precision and sparseness. The model with the wavelet and hybrid kernel has a much faster convergence rate compared to that with a conventional Radial Basis Function (RBF) kernel.
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