2007
NeurIPS
NeurIPS 2007
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.
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Interdisciplinary Bridge
- Artificial Intelligence and Mathematics & Optimization
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Trend Setter
- Regression
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Keyword Pioneer
- additive model
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Hot Topic Early Bird
- high-dimensional data
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Regression
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Core Methods > Feature Selection
Machine Learning > Learning Types > Supervised Learning
Mathematics & Optimization > Optimization > Sparse Optimization
Machine Learning > Optimization & Theory > Sparse Optimization
Machine Learning > Learning Types > Sparse Learning
Deep Learning > Learning Types > Regression