Nonparametric regression and classification with joint sparsity constraints

We propose new families of models and algorithms for high-dimensional nonparametric learning with joint sparsity constraints. Our approach is based on a regularization method that enforces common sparsity patterns across different function components in a nonparametric additive model. The algorithms employ a coordinate descent approach that is based on a functional soft-thresholding operator. The methods are illustrated with experiments on synthetic data and gene microarray data. Papers published at the Neural Information Processing Systems Conference.