  We propose a new method for supervised learning. The hubNet procedure fits a
hub-based graphical model to the predictors, to estimate the amount of
"connection" that each predictor has with other predictors. This yields a set
of predictor weights that are then used in a regularized regression such as the
lasso or elastic net. The resulting procedure is easy to implement, can
sometimes yields higher prediction accuracy that the lasso, and can give
insights into the underlying structure of the predictors. HubNet can also be
generalized seamlessly to other supervised problems such as regularized
logistic regression (and other GLMs), Cox's proportional hazards model, and
nonlinear procedures such as random forests and boosting. We prove some
recovery results under a specialized model and illustrate the method on real
and simulated data.
