SpAM: Sparse Additive Models

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Han Liu, Larry Wasserman, John Lafferty, Pradeep Ravikumar


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.