A ridgelet kernel approach for regression using particle swarm optimization algorithm

In this paper, a ridgelet kernel approach 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. Taking the objective function solved by quadratic programming to define a fitness function, we use particle swarm optimization algorithm to optimize the directions of ridgelets. Experiments in the tasks of regression prove its efficiency.