Two important elements have driven recent innovation in the field of regression: sparsity-inducing regularization, to cope with high-dimensional problems; multi-task learning through joint parameter estimation, to augment the number of training samples. Both approaches complement each other in the sense that a joint estimation results in more samples, which are needed to estimate sparse models accurately, whereas sparsity promotes models that act on subsets of related variables. This idea has driven the proposal of block regularizers such as L1/Lq norms, which however effective, require that active regressors strictly overlap. In this paper, we propose a more flexible convex regularizer based on unbalanced optimal transport (OT) theory. That regularizer promotes parameters that are close, according to the OT geometry, which takes into account a prior geometric knowledge on the regressor variables. We derive an efficient algorithm based on a regularized formulation of optimal transport, which iterates through applications of Sinkhorn's algorithm along with coordinate descent iterations. The performance of our model is demonstrated on regular grids and complex triangulated geometries of the cortex with an application in neuroimaging.
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