10.1184/R1/6607766.v1 Han Liu Han Liu John Lafferty John Lafferty Larry Wasserman Larry Wasserman Nonparametric Regression and Classification with Joint Sparsity Constraints Carnegie Mellon University 2005 computer sciences 2005-06-01 00:00:00 Journal contribution https://kilthub.cmu.edu/articles/journal_contribution/Nonparametric_Regression_and_Classification_with_Joint_Sparsity_Constraints/6607766 We 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.