This paper deals with a nonparametric method for estimating the ridges of a density function. Ridge estimation is useful for understanding the structure of a density. It can also be used to find hidden structure in point cloud data: when the data are noisy measurements of a manifold, under mild conditions the ridges are close and topologically similar to the hidden manifold. We propose a new estimation procedure called SuRF and study its rate of convergence.

SuRF: Subspace Ridge Finder / PERONE PACIFICO, Marco. - Cladag 2013. 9th Meeting of the Classification and Data Analysis Group. Book of Abstracts, (2013), pp. 357-360. (9th Meeting of the Classification and Data Analysis Group

SuRF: Subspace Ridge Finder

PERONE PACIFICO, MARCO
2013

Abstract

This paper deals with a nonparametric method for estimating the ridges of a density function. Ridge estimation is useful for understanding the structure of a density. It can also be used to find hidden structure in point cloud data: when the data are noisy measurements of a manifold, under mild conditions the ridges are close and topologically similar to the hidden manifold. We propose a new estimation procedure called SuRF and study its rate of convergence.
9788867871179
mean shift; manifold learning; ridges; density estimation
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11385/182614
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