Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Adaptive Noisy Clustering

Abstract : The problem of adaptive noisy clustering is investigated. Given a set of noisy observations $Z_i=X_i+\epsilon_i$, $i=1, \ldots, n$, the goal is to design clusters associated with the law of $X_i$'s, with unknown density $f$ with respect to the Lebesgue measure. Since we observe a corrupted sample, a direct approach as the popular $k$-means is not suitable in this case. In this paper, we propose a noisy $k$-means minimization, which is based on the $k$-means loss function and a deconvolution estimator of the density $f$. In particular, this approach suffers from the dependance on a bandwidth involved in the deconvolution kernel. Fast rates of convergence for the excess risk are proposed for a particular choice of the bandwidth, which depends on the smoothness of the density $f$. Then, we turn out into the main issue of the paper: the data-driven choice of the bandwidth. We state an adaptive upper bound for a new selection rule, called ERC (Empirical Risk Comparison). This selection rule is based on the Lepski's principle, where empirical risks associated with different bandwidths are compared. Finally, we illustrate that this adaptive rule can be used in many statistical problems of $M$-estimation where the empirical risk depends on a nuisance parameter.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00831672
Contributor : Sébastien Loustau <>
Submitted on : Friday, June 7, 2013 - 2:44:32 PM
Last modification on : Monday, March 9, 2020 - 6:16:03 PM
Document(s) archivé(s) le : Sunday, September 8, 2013 - 4:20:02 AM

File

adaptivenoisyclusteringmai2013...
Publisher files allowed on an open archive

Identifiers

  • HAL Id : hal-00831672, version 1

Citation

Michael Chichignoud, Sébastien Loustau. Adaptive Noisy Clustering. 2013. ⟨hal-00831672⟩

Share

Metrics

Record views

571

Files downloads

301