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On Iterative Hard Thresholding Methods for High-dimensional M-Estimation

NeurIPS 2014 • Prateek Jain • Ambuj Tewari • Purushottam Kar

The use of M-estimators in generalized linear regression models in high dimensional settings requires risk minimization with hard $L_0$ constraints. Of the known methods, the class of projected gradient descent (also known as iterative hard thresholding (IHT)) methods is known to offer the fastest and most scalable solutions... (read more)

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METHOD TYPE
Linear Regression
Generalized Linear Models
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