7 citations · 7 across the 2 of their papers we have counts for
4 papers · 1 filter
Optimal quantization of the mean measure and applications to statistical learning
Frédéric Chazal, Clément Levrard, Martin Royer
This paper addresses the case where data come as point sets, or more generally as discrete measures. Our motivation is twofold: first we intend to approximate with a compactly supp…
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…
The k-PDTM : a coreset for robust geometric inference
Claire Brécheteau, Clément Levrard
Analyzing the sub-level sets of the distance to a compact sub-manifold of R d is a common method in TDA to understand its topology. The distance to measure (DTM) was introduced by…
Quantization/clustering: when and why does k-means work?
Clément Levrard
Though mostly used as a clustering algorithm, k-means are originally designed as a quantization algorithm. Namely, it aims at providing a compression of a probability distribution…