- Date de réalisation : 2 Juillet 2019
- Lieu de réalisation : École Normale Supérieure, Paris.
- Durée du programme : 43 min
- Classification Dewey : Statistique mathématique
- Auteur(s) : Verzelen Nicolas
- producteur : Boyer Claire, Chafaï Djalil, Lehec Joseph
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Verzelen - Clustering with the relaxed K-means
This talk is devoted to clustering problems. It amounts to partitionning a set of given points or the nodes of a given graph, in such a way that the groups are as homogeneous as possible. After introducing two random instances of this problem, namely sub-Gaussian Mixture Model (sGMM) and Stochastic Block Model (SBM), I will explain how convex relaxations of the classical $K$-means criterion achieve near optimal performances. Emphasis will be put on the connections between the clustering bounds and relevant results in random matrix theory.