Ridgelet kernel regression

In this paper, a ridgelet kernel regression model is proposed for approximation of high dimensional functions. It is based on ridgelet theory, kernel and regularization technology from which we can deduce a regularized kernel regression form. Taking the objective function solved by quadratic programming to define the fitness function, we use genetic algorithm to search for the optimal directional vector of ridgelet. The results indicate that this method can effectively deal with high dimensional data, especially those with certain kinds of spatial inhomogeneities. Some illustrative examples are included to demonstrate its superiority.

• Type: Journal
• Year: 2007
• Where: IJON
• Authors: Shuyuan Yang, Min Wang, Licheng Jiao
• Keywords: IJON 2007, Kernel Regression, Kernel Regression Model, Regularized Kernel Regression