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SpAM: Sparse Additive Models
Pradeep Ravikumar · Han Liu · John Lafferty · Larry Wasserman

Tue Dec 04 09:50 AM -- 10:00 AM (PST) @

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.

Author Information

Pradeep Ravikumar (Carnegie Mellon University)
Han Liu (Carnegie Mellon University)
John Lafferty (Yale University)
Larry Wasserman (Carnegie Mellon University)

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