Structured data analysis with Regularized Generalized Canonical Correlation Analysis

A. Tenenhaus
Habilitation à Diriger des Recherches (HDR) le 5 Janvier 2016, 09h30 à CentraleSupelec (Gif-sur-Yvette) Amphi F3-06

The challenges related to the use of massive amounts of data (e.g omics data, imaging-genetic data, etc)
include identifying the relevant variables, reducing dimensionality, summarizing information in a comprehensible way
and displaying it for interpretation purposes. Often, these data are intrinsically structured in blocks of variables, in groups
of individuals or in tensor. Classical statistical tools cannot be applied without altering their structure leading to the risk of
information loss. The need to analyze the data by taking into account their natural structure appears to be essential but
requires the development of new statistical techniques that constitutes the core of my research for many years.
In particular, I am interested in multiblock, multigroup and multiway structures. In that context a general framework
for structured data analysis based on Regularized Generalized Canonical Correlation Analysis (RGCCA) is defined.

Membres du Jury :

* Hervé Abdi, Professeur, Université of Texas, Rapporteur
* Florence d’Alché-Buc, Professeur, Telecom ParisTech, Rapporteur
* Jean-Philippe Vert, Directeur de Recherche, Mines ParisTech, Rapporteur
* Mohamed Hanafi, Ingénieur-Chercheur, ONIRIS, Examinateur
* Jean-Michel Poggi, Professeur, Paris V, Examinateur
* Gilbert Saporta, Professeur, CNAM, Examinateur