Improving sparse recovery on structured images with bagged clustering

Andrés Hoyos-Idrobo (1, 2) , Yannick Schwartz (1, 2) , Gaël Varoquaux (1, 2) , Bertrand Thirion (2, 1)

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
2 NEUROSPIN - Service NEUROSPIN

—The identification of image regions associated with external variables through discriminative approaches yields ill-posed estimation problems. This estimation challenge can be tackled by imposing sparse solutions. However, the sensitivity of sparse estimators to correlated variables leads to non-reproducible results, and only a subset of the important variables are selected. In this paper, we explore an approach based on bagging clustering-based data compression in order to alleviate the instability of sparse models. Specifically, we design a new framework in which the estimator is built by averaging multiple models estimated after feature clustering, to improve the conditioning of the model. We show that this combination of model averaging with spatially consistent compression can have the virtuous effect of increasing the stability of the weight maps, allowing a better interpretation of the results. Finally, we demonstrate the benefit of our approach on several predictive modeling problems.

Mots clés: Stability; High-dimensional estimators; machine learning; brain imaging; clustering

Domaines: Machine Learning [stat.ML]; Neurosciences

Andrés Hoyos-Idrobo, Yannick Schwartz, Gaël Varoquaux, Bertrand Thirion. Improving sparse recovery on structured images with bagged clustering. International Workshop On Pattern Recognition In Neuroimaging (PRNI), 2015, Jun 2015, Palo alto, United States. ⟨hal-01174335⟩