SpAM: Sparse additive models

Pradeep Ravikumar, Han Liu, John Lafferty, Larry Wasserman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

51 Scopus citations

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 derive 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, showing that SpAM can be effective in fitting sparse nonparametric models in high dimensional data.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference
StatePublished - Dec 1 2009
Event21st Annual Conference on Neural Information Processing Systems, NIPS 2007 - Vancouver, BC, Canada
Duration: Dec 3 2007Dec 6 2007

Publication series

NameAdvances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

Other

Other21st Annual Conference on Neural Information Processing Systems, NIPS 2007
CountryCanada
CityVancouver, BC
Period12/3/0712/6/07

All Science Journal Classification (ASJC) codes

  • Information Systems

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    Ravikumar, P., Liu, H., Lafferty, J., & Wasserman, L. (2009). SpAM: Sparse additive models. In Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference (Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference).