Rates of convergence for robust geometric inference - Archive ouverte HAL Access content directly
Journal Articles Electronic Journal of Statistics Year : 2016

Rates of convergence for robust geometric inference

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Abstract

Distances to compact sets are widely used in the field of Topological Data Analysis for inferring geometric and topological features from point clouds. In this context, the distance to a probability measure (DTM) has been introduced by Chazal et al. (2011b) as a robust alternative to the distance a compact set. In practice, the DTM can be estimated by its empirical counterpart, that is the distance to the empirical measure (DTEM). In this paper we give a tight control of the deviation of the DTEM. Our analysis relies on a local analysis of empirical processes. In particular, we show that the rate of convergence of the DTEM directly depends on the regularity at zero of a particular quantile function which contains some local information about the geometry of the support. This quantile function is the relevant quantity to describe precisely how difficult is a geometric inference problem. Several numerical experiments illustrate the convergence of the DTEM and also confirm that our bounds are tight.
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Dates and versions

hal-01336913 , version 1 (24-06-2016)
hal-01336913 , version 2 (19-10-2016)

Identifiers

  • HAL Id : hal-01336913 , version 2

Cite

F Chazal, P Massart, B Michel. Rates of convergence for robust geometric inference. Electronic Journal of Statistics , 2016, 10 (2), pp.44. ⟨hal-01336913v2⟩
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