[Statlist] Research seminar in statistics February 27th 2015, GSEM University of Geneva

[Eva Cantoni]

Organisers :
E. Cantoni - E. Ronchetti - S. Sperlich - M-P. Victoria-Feser

Friday February 27th, 2015
at 11h15 - Room M 5220, Uni Mail (40, bd du Pont-d'Arve)

Julie Josse
Agrocampus Ouest, Rennes

ABSTRACT :
Low-rank matrix estimation plays a key role in many scientific and 
engineering tasks including collaborative filtering and image 
denoising.  Low-rank procedures are often motivated by the statistical 
model where we observe a noisy matrix drawn from some distribution with 
expectation assumed to have a low-rank representation. The statistical 
goal is to try to recover the signal from the noisy data. Classical 
approaches are centered around singular-value decomposition algorithms. 
Although the truncated singular value decomposition has been extensively 
used and studied, the estimator is found to be noisy and its performance 
can be improved by regularization. Methods based on singular-value 
shrinkage have achieved considerable empirical success and also have 
provable optimality properties in the Gaussian noise model (Gavish & 
Donoho, 2014). In this presentation, we propose a new framework for 
regularized low-rank estimation that does not start from the 
singular-value shrinkage point of view. Our approach is motivated by a 
simple parametric boostrap idea. In the simplest case of isotropic 
Gaussian noise, we end up with a new singular-value shrinkage estimator 
whereas for non-isotropic noise models, our procedure yields new 
estimators that perform well in experiments. This is a joint work with 
Stefan Wager.

Visit the website: http://www.stat-center.unige.ch/ressem.html

-- 
Prof. Eva Cantoni
Research Center for Statistics and
      Geneva School of Economics and Management
University of Geneva, Bd du Pont d'Arve 40, CH-1211 Genève 4
http://stat-center.unige.ch/members2/profs/eva-cantoni/