High dimensional thresholded regression and shrinkage effect

11 May 2016Zemin ZhengYingying FanJinchi Lv

High-dimensional sparse modeling via regularization provides a powerful tool for analyzing large-scale data sets and obtaining meaningful, interpretable models. The use of nonconvex penalty functions shows advantage in selecting important features in high dimensions, but the global optimality of such methods still demands more understanding... (read more)

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