Authors: Meng Zhang, Jiaogen Zhou, Lihua Fu, Tingting He
Addresses: Department of Computer Science, Central China Normal University, Wuhan, China. ' National Engineering Research Center, for Information Technology in Agriculture (NRECITA), Beijing, China. ' School of Mathematics and Physics, Chinese University of Geosciences, Wuhan, China. ' Department of Computer Science, Central China Normal University, Wuhan, China
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.
Keywords: orthogonal least squares; OLS; hybrid wavelet kernel; repeated weighted boosting search; RWBS; sparse regression modelling; generalised kernel models; forward selection.
International Journal of Business Intelligence and Data Mining, 2008 Vol.3 No.4, pp.437 - 450
Available online: 25 Jan 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article