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NIPS
2007

SpAM: Sparse Additive Models

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SpAM: Sparse Additive Models
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.
Pradeep D. Ravikumar, Han Liu, John D. Lafferty, L
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2007
Where NIPS
Authors Pradeep D. Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman
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