Computationally Efficient Exact Calculation of Kernel Density Derivatives

Machine learning research related to the derivatives of the kernel density estimator has received limited attention compared to the density estimator itself. This is despite of the general consensus that most of the important features of a data distribution, such as modes, curvature or even cluster structure, are characterized by its derivatives. In this paper we present a computationally efficient algorithm to calculate kernel density estimates and their derivatives for linearly separable kernels, with significant savings especially for high dimensional data and higher order derivatives. It significantly reduces the number of operations (multiplications and derivative evaluations) to calculate the estimates, while keeping results exact (i.e. no approximations are involved). The main idea is that the calculation of multivariate separable kernels and their derivatives, such as the gradient vector and the Hessian matrix involves significant number of redundant operations that can be eliminated using the chain rule. A tree-based algorithm that calculates exact kernel density estimate and derivatives in the most efficient fashion is presented with the particular focus being on optimizing kernel evaluations for individual data pairs. In contrast, most approaches in the literature resort to approximations of functions or downsampling. Overall computational savings of the presented method could be further increased by incorporating such approximations, which aim to reduce the number of pairs of data considered. The theoretical computational complexity of the tree-based and direct methods that perform all multiplications are compared. In experimental results, calculating separable kernels and their derivatives is considered, as well as a measure that evaluates how close a point is to the principal curve of a density, which employs first and second derivatives. These results indicate considerable improvement in computational complexity, hence time over the direct approach.