Learning with latent group sparsity via heat flow dynamics on networks. (arXiv:2201.08326v1 [stat.ME])

Group or cluster structure on explanatory variables in machine learning
problems is a very general phenomenon, which has attracted broad interest from
practitioners and theoreticians alike. In this work we contribute an approach
to learning under such group structure, that does not require prior information
on the group identities. Our paradigm is motivated by the Laplacian geometry of
an underlying network with a related community structure, and proceeds by
directly incorporating this into a penalty that is effectively computed via …

arxiv learning networks sparsity