Learning with Clustering Structure

Vincent Roulet (1) , Fajwel Fogel (1, 2) , Alexandre d'Aspremont (1, 2) , Francis Bach (1, 2)

1 LIENS - Laboratoire d'informatique de l'école normale supérieure
2 SIERRA - Statistical Machine Learning and Parsimony

We study a supervised clustering problem seeking to cluster either features, tasks or sample points using losses extracted from supervised learning problems. We formulate a unified optimization problem handling these three settings and derive algorithms whose core iteration complexity is concentrated in a k-means clustering step, which can be approximated efficiently. We test our methods on both artificial and realistic data sets extracted from movie reviews and 20NewsGroup.

Domaines: Recherche opérationnelle [math.OC]; Intelligence artificielle [cs.AI]; Machine Learning [stat.ML]

Vincent Roulet, Fajwel Fogel, Alexandre d'Aspremont, Francis Bach. Learning with Clustering Structure. 2016. ⟨hal-01239305⟩