Soumendu Sunder Mukherjee / Learning under latent group sparsity via heat flow dynamics on networks

Описание: Title: Learning under latent group sparsity via heat flow dynamics on networks

Abstract: In this talk, we will consider the problem of variable selection in high-dimensional regression under latent group sparsity. We will present a new penalty that automatically selects variables in groups without being explicitly told what those groups are. This will be done by incorporating into the penalty a suitable Laplacian matrix (containing group information) in the form of a heat flow. At equilibrium, the proposed penalty coincides with the classical group lasso penalty. We will present some numerical and theoretical results on the performance of the proposed penalty. This is based on joint work with Subhroshekhar Ghosh.