Nonparametric Regression and Classification with Joint Sparsity Constraints
journal contributionposted on 01.06.2005 by Han Liu, John Lafferty, Larry Wasserman
Any type of content formally published in an academic journal, usually following a peer-review process.
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.