Data Institute Seminar: co-clustering through optimal transport by Charlotte Laclau (LIG)

on the September 21, 2017

21 september at 14:00
A novel method for co-clustering will be presented : an unsupervised learning approach that aims at discovering homogeneous groups of data instances and features by grouping them simultaneously.
The proposed method uses the entropy regularized optimal transport between empirical measures defined on data instances and features in order to obtain an estimated joint probability density function represented by the optimal coupling matrix. This matrix is further factorized to obtain the induced row and columns partitions using multiscale representations approach. To justify our method theoretically, we show how the solution of the regularized optimal transport can be seen from the variational inference perspective thus motivating its use for co-clustering. The algorithm derived for the proposed method and its kernelized version based on the notion of Gromov-Wasserstein distance are fast, accurate and can determine automatically the number of both row and column clusters. These features are vividly demonstrated through extensive experimental evaluations.

Seminar organised by Caroline Bazzoli and Vincent Brault, LJK.

Published on September 15, 2017

Practical informations


Salle séminaire 1 et 2 RDC - Batiment IMAG. Accès public.