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math.ST2019★ 1 cited
Estimation via length-constrained generalized empirical principal curves under small noise
Sylvain Delattre, Aurélie Fischer
In this paper, we propose a method to build a sequence of generalized empirical principal curves, with selected length, so that, in Hausdor distance, the images of the estimating p…
math.ST2018
Robust Bregman Clustering
Aurélie Fischer, Clément Levrard, Claire Brécheteau
Using a trimming approach, we investigate a k-means type method based on Bregman divergences for clustering data possibly corrupted with clutter noise. The main interest of Bregman…
math.ST2018
Convergence rates for smooth k-means change-point detection
Aurélie Fischer, Dominique Picard
In this paper, we consider the estimation of a change-point for possibly high-dimensional data in a Gaussian model, using a k-means method. We prove that, up to a logarithmic term,…