SpAM: Sparse Additive Models
Abstract
We present a new class of models for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We de- rive a method for fitting the models that is effective even when the number of covariates is larger than the sample size. A statistical analysis of the properties of SpAM is given together with empirical results on synthetic and real data, show- ing that SpAM can be effective in fitting sparse nonparametric models in high dimensional data.
Cite
Text
Liu et al. "SpAM: Sparse Additive Models." Neural Information Processing Systems, 2007.Markdown
[Liu et al. "SpAM: Sparse Additive Models." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/liu2007neurips-spam/)BibTeX
@inproceedings{liu2007neurips-spam,
title = {{SpAM: Sparse Additive Models}},
author = {Liu, Han and Wasserman, Larry and Lafferty, John D. and Ravikumar, Pradeep K.},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {1201-1208},
url = {https://mlanthology.org/neurips/2007/liu2007neurips-spam/}
}