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.