Flexible Low-Rank Statistical Modeling with Missing Data and Side Information
Flexible Low-Rank Statistical Modeling with Missing Data and Side Information
We explore a general statistical framework for low-rank modeling of matrix-valued data, based on convex optimization with a generalized nuclear norm penalty. We study several related problems: the usual low-rank matrix completion problem with flexible loss functions arising from generalized linear models; reduced-rank regression and multi-task learning; and generalizations of …