Unsupervised learning for multivariate probability density estimation: radial basis and projection pursuit

Two types of unsupervised learning techniques for nonparametric multivariate density estimation are discussed, where no assumption is made about the data being drawn from any of known parametric families of distribution. The first type is based on a robust kernel method which uses locally tuned radial basis (Gaussian) functions. The second type is based on an exploratory projection pursuit technique which uses orthogonal polynomial approximation to 1-D density along several projections from multidimensional data. Performance evaluations using training data from mixture Gaussian and mixture Cauchy densities are presented