# Big Data: Modeling, Estimation and Selection

Jun 9 – 10, 2016
Ecole Centrale Lille
Europe/Paris timezone

## Construction of tight wavelet-like frames on graphs for denoising

Jun 10, 2016, 11:35 AM
45m
Grand Amphithéâtre (Ecole Centrale Lille)

### Grand Amphithéâtre

#### Ecole Centrale Lille

Campus Lille 1 à Villeneuve d'Ascq

### Speaker

Gilles Blanchard (University of Potsdam)

### Description

We construct a frame (redundant dictionary) for the space of real-valued functions defined on a neighborhood graph constructed from data points. This frame is adapted to the underlying geometrical structure (e.g. the points belong to an unknown low dimensional manifold), has finitely many elements, and these elements are localized in frequency as well as in space. This construction follows the ideas of Hammond et al. (2011), with the key point that we construct a tight (or Parseval) frame. This means we have a very simple, explicit reconstruction formula for every functiondefined on the graph from the coefficients given by its scalar product with the frame elements. We use this representation in the setting of denoising where we are given noisy observations of a functiondefined on the graph. By applying a thresholding method to the coefficients in the reconstruction formula, we define an estimate ofwhose risk satisfies a tight oracle inequality.

### Presentation materials

 Transparents