Learning Single Index Models in High Dimensions

  title={Learning Single Index Models in High Dimensions},
  author={Ravi Ganti and Nikhil S. Rao and Rebecca Willett and Robert D. Nowak},
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 described to learn SIMs in the low dimensional regime, a method that can efficiently learn SIMs in high dimensions has not been forthcoming. We propose three… CONTINUE READING
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