Nonparametric Regression and Classification with Joint Sparsity Constraints
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.
Cite
Text
Liu et al. "Nonparametric Regression and Classification with Joint Sparsity Constraints." Neural Information Processing Systems, 2008.Markdown
[Liu et al. "Nonparametric Regression and Classification with Joint Sparsity Constraints." Neural Information Processing Systems, 2008.](https://mlanthology.org/neurips/2008/liu2008neurips-nonparametric/)BibTeX
@inproceedings{liu2008neurips-nonparametric,
title = {{Nonparametric Regression and Classification with Joint Sparsity Constraints}},
author = {Liu, Han and Wasserman, Larry and Lafferty, John D.},
booktitle = {Neural Information Processing Systems},
year = {2008},
pages = {969-976},
url = {https://mlanthology.org/neurips/2008/liu2008neurips-nonparametric/}
}