Memory-efficient orthogonal least squares kernel density estimation using enhanced empirical cumulative distribution functions

A novel training algorithm for sparse kernel density estimates by regression of the empirical cumulative density function (ECDF) is presented. It is shown how an overdetermined linear least-squares problem may be solved by a greedy forward selection procedure using updates of the orthogonal decomposition in an order-recursive manner. We also present a method for improving the accuracy of the estimated models which uses output-sensitive computation of the ECDF. Experiments show the superior performance of our proposed method compared to state-of-the-art density estimation methods such a