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.
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Titolo: | SuRF: Subspace Ridge Finder |
Autori: | |
Data di pubblicazione: | 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. |
Handle: | http://hdl.handle.net/11385/182614 |
ISBN: | 9788867871179 |
Appare nelle tipologie: | 04.1 - Contributo in Atti di convegno (Paper in Proceedings) |
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