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Download fileNonparametric Regression and Classification with Joint Sparsity Constraints
journal contribution
posted on 2005-06-01, 00:00 authored by Han Liu, John Lafferty, Larry WassermanWe propose new families of models and algorithms for high-dimensional nonpara-
metric learning with joint sparsity constraints. Our approach is based on a regular-
ization method that enforces common sparsity patterns across different function
components in a nonparametric additive model. The algorithms employ a coor-
dinate 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.