Anisotropic k-Nearest Neighbor Search Using Covariance Quadtree

We present a variant of the hyper-quadtree that divides a multidimensional space according to the hyperplanes associated to the principal components of the data in each hyperquadrant. Each of the 2 hyper-quadrants is a data partition in a λ-dimension subspace, whose intrinsic dimensionality λ ≤ d is reduced from the root dimensionality d by the principal components analysis, which discards the irrelevant eigenvalues of the local covariance matrix. In the present method a component is irrelevant if its length is smaller than, or comparable to, the local inter-data spacing. Thus, the covariance hyperquadtree is fully adaptive to the local dimensionality. The proposed data-structure is used to compute the anisotropic K nearest neighbors (kNN), supported by the Mahalanobis metric. As an application, we used the present k nearest neighbors method to perform density estimation over a noisy data distribution. Such estimation method can be further incorporated to the smoothed particle hydrodynamics, allowing computer simulations of anisotropic fluid flows.