Density estimation from unweighted k-nearest neighbor graphs: a roadmap Density estimation from unweighted k-nearest neighbor graphs: a roadmap
Paper summary A method of estimating a density (up to constants) from an unweighted, directed k nearest neighbor graph is described. It is assumed (more or less) that the density is continuously differentiable, supported on a compact and connected subset of $R^d$ with non-empty interior and a smooth boundary, and is upper- and lower-bounded on its support.
papers.nips.cc
scholar.google.com
Density estimation from unweighted k-nearest neighbor graphs: a roadmap
von Luxburg, Ulrike and Alamgir, Morteza
Neural Information Processing Systems Conference - 2013 via Bibsonomy
Keywords: dblp


Loading...
Your comment:


Short Science allows researchers to publish paper summaries that are voted on and ranked!
About