Application Domains
New Software and Platforms
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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## Section: New Results

### Robust non Gaussian models

#### Robust Locally linear mapping with mixtures of Student distributions

Participants : Florence Forbes, Emeline Perthame, Brice Olivier.

The standard GLLiM model [6] for high dimensional regression assumes Gaussian noise models and is in its unconstrained version equivalent to a joint GMM. The fact that response and independent variables $\left(X,Y\right)$ are jointly a mixture of Gaussian distribution is the key for all derivations in the model. In this work, we show that similar developments are possible based on a joint Student Mixture model, joint SMM. It follows a new model referred to as SLLiM for Student Locally linear mapping for which we investigate the robustness to outlying data in a high dimensional regression context [71]. The corresponding code is available on the CRAN in the xLLiM package.

#### Rectified binaural ratio: A complex T-distributed feature for robust sound localization

Participant : Florence Forbes.

Joint work with: Antoine Deleforge, Inria panama team in Rennes.

#### Statistical reconstruction methods for multi-energy tomography

Participants : Florence Forbes, Pierre-Antoine Rodesch.

Joint work with: Veronique Rebuffel from CEA Grenoble.

In the context of Pierre-Antoine Rodesh's PhD thesis, we investigate new statistical and optimization methods for tomographic reconstruction from non standard detectors providing multiple energy signals.