nips nips2008 nips2008-155 nips2008-155-reference knowledge-graph by maker-knowledge-mining
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Author: Han Liu, Larry Wasserman, John D. Lafferty
Abstract: 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. 1
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