A novel ridgelet kernel regression method

Authors:
Shuyuan Yang;Min Wang;Licheng Jiao;Qing Li
Affiliations:
Institute of Intelligence Information Processing, Xidian University, Xi'an, Shaanxi, China;Institute of Intelligence Information Processing, Xidian University, Xi'an, Shaanxi, China;Institute of Intelligence Information Processing, Xidian University, Xi'an, Shaanxi, China;Institute of Intelligence Information Processing, Xidian University, Xi'an, Shaanxi, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

In this paper, a ridgelet kernel regression model is proposed for approximation of multivariate functions, especially those with certain kinds of spatial inhomogeneities. It is based on ridgelet theory, kernel and regularization technology from which we can deduce a regularized kernel regression form. Using the objective function solved by quadratic programming to define a fitness function, we adopt particle swarm optimization algorithm to optimize the directions of ridgelets. Theoretical analysis proves the superiority of ridgelet kernel regression for multivariate functions. Experiments in regression indicate that it not only outperforms support vector machine for a wide range of multivariate functions, but also is robust and quite competitive on training of time.