Nonparametric regression and classification with joint sparsity constraints

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Authors

Han Liu, Larry Wasserman, John Lafferty

Abstract

<p>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 framework yields several new models, including multi-task sparse additive models, multi-response sparse additive models, and sparse additive multi-category logistic regression. The methods are illustrated with experiments on synthetic data and gene microarray data.</p>