11 citations · 11 across the 1 of their papers we have counts for
5 papers
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…
ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning
Martin Royer, Frédéric Chazal, Clément Levrard +2
Robust topological information commonly comes in the form of a set of persistence diagrams, finite measures that are in nature uneasy to affix to generic machine learning framework…
PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures
Mathieu Carrière, Frédéric Chazal, Yuichi Ike +3
Persistence diagrams, the most common descriptors of Topological Data Analysis, encode topological properties of data and have already proved pivotal in many different applications…
Adaptive Clustering through Semidefinite Programming
Martin Royer
We analyze the clustering problem through a flexible probabilistic model that aims to identify an optimal partition on the sample X 1 , ..., X n. We perform exact clustering with h…
PECOK: a convex optimization approach to variable clustering
Florentina Bunea, Christophe Giraud, Martin Royer +1
The problem of variable clustering is that of grouping similar components of a -dimensional vector , and estimating these groups from independent cop…