Canal-U

Mon compte
CEREMADE - UMR 7534

Verzelen - Clustering with the relaxed K-means


Copier le code pour partager la vidéo :
<div style="position:relative;padding-bottom:56.25%;padding-top:10px;height:0;overflow:hidden;"><iframe src="https://www.canal-u.tv/video/ceremade/embed.1/verzelen_clustering_with_the_relaxed_k_means.53799?width=100%&amp;height=100%" style="position:absolute;top:0;left:0;width:100%;height: 100%;" width="550" height="306" frameborder="0" allowfullscreen scrolling="no"></iframe></div> Si vous souhaitez partager une séquence, indiquez le début de celle-ci , et copiez le code : h m s
Auteur(s) :
Verzelen Nicolas

Producteur Canal-U :
CEREMADE - UMR 7534
Contacter le contributeur
J’aime
Imprimer
partager facebook twitter Google +

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.

  •  
  •  
    Date de réalisation : 2 Juillet 2019
    Lieu de réalisation : École Normale Supérieure, Paris.
    Durée du programme : 43 min
    Classification Dewey : Statistique mathématique
  •  
    Catégorie : Conférences, Cours magistraux, Séminaires
    Niveau : niveau Doctorat (LMD), Recherche
    Disciplines : Statistiques
    Collections : PSL Summer School on High Dimensional Probability and Algorithms - HDPA 2019
    ficheLom : Voir la fiche LOM
  •  
    Auteur(s) : Verzelen Nicolas
    producteur : Boyer Claire, Chafaï Djalil, Lehec Joseph
  •  
    Langue : Anglais
    Mots-clés : mixtures, clustering, kmeans, stochastic bloc model
 

commentaires


Ajouter un commentaire Lire les commentaires
*Les champs suivis d’un astérisque sont obligatoires.
Aucun commentaire sur cette vidéo pour le moment (les commentaires font l’objet d’une modération)
 

Dans la même collection

 Tropp 9/9 - Random matrix theory and computational linear algebra
 Tropp 8/9 - Random matrix theory and computational linear algebra
 Carpentier - Introduction to some problems of composite and minimax hypothesis testing
 Tropp 7/9 - Random matrix theory and computational linear algebra
 Tropp 6/9 - Random matrix theory and computational linear algebra
 Bubeck 9/9 - Some geometric aspects of randomized online decision making
 Bubeck 8/9 - Some geometric aspects of randomized online decision making
 Zdeborová - Loss landscape and behaviour of algorithms in the spiked matrix-tensor model
 Tropp 5/9 - Random matrix theory and computational linear algebra
 Bubeck 7/9 - Some geometric aspects of randomized online decision making
 Bubeck 6/9 - Some geometric aspects of randomized online decision making
 Bubeck 5/9 - Some geometric aspects of randomized online decision making
 Massoulié - Planting trees in graphs, and finding them back
 Tropp 4/9 - Random matrix theory and computational linear algebra
 Tropp 3/9 - Random matrix theory and computational linear algebra
 Bubeck 3/9 - Some geometric aspects of randomized online decision making
 Klopp - Sparse Network Estimation
 De Castro - Spectral convergence of random graphs and a focus on random geometric graphs
 Tropp 2/9 - Random matrix theory and computational linear algebra
 Tropp 1/9 - Random matrix theory and computational linear algebra
 Bubeck 4/9 - Some geometric aspects of randomized online decision making
 Bubeck 2/9 - Some geometric aspects of randomized online decision making
 Bubeck 1/9 - Some geometric aspects of randomized online decision making
FMSH
 
Facebook Twitter Google+
Mon Compte