Learning Single Index Models in High Dimensions

CoRR, Volume abs/1506.08910, 2015.

Cited by: 14|Bibtex|Views8|Links
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Abstract:

Single Index Models (SIMs) are simple yet flexible semi-parametric models for classification and regression. Response variables are modeled as a nonlinear, monotonic function of a linear combination of features. Estimation in this context requires learning both the feature weights, and the nonlinear function. While methods have been des...More

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