Robust Sparse Reduced Rank Regression in High Dimensions

18 Oct 2018Kean Ming TanQiang SunDaniela Witten

We propose robust sparse reduced rank regression for analyzing large and complex high-dimensional data with heavy-tailed random noise. The proposed method is based on a convex relaxation of a rank- and sparsity-constrained non-convex optimization problem, which is then solved using the alternating direction method of multipliers algorithm... (read more)

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