Kernel density estimation on Riemannian manifolds
نویسنده
چکیده
The estimation of the underlying probability density of n i.i.d. random objects on a compact Riemannian manifold without boundary is considered. The proposed methodology adapts the technique of kernel density estimation on Euclidean sample spaces to this non-Euclidean setting. Under sufficient regularity assumptions on the underlying density, L 2 convergence rates are obtained. Index Terms — Nonparametric density estimation, Kernel density estimation, Riemannian manifolds, L2 convergence.
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تاریخ انتشار 2006