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Nonparametric regression and classification with joint sparsity constraints
Han Liu · John Lafferty · Larry Wasserman

Wed Dec 10 05:20 PM -- 05:21 PM (PST) @ None

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.

Author Information

Han Liu (Carnegie Mellon University)
John Lafferty (Yale University)
Larry Wasserman (Carnegie Mellon University)

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