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Simultaneous Best Subset Selection and Dimension Reduction via Primal-Dual Iterations
Sparse reduced rank regression is an essential statistical learning method.
Parallel integrative learning for large-scale multi-response regression with incomplete outcomes
In this paper, we propose a scalable and computationally efficient procedure, called PEER, for large-scale multi-response regression with incomplete outcomes, where both the numbers of responses and predictors can be high-dimensional.
Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix
In the first stage of division, we consider both sequential and parallel approaches for simplifying the task into a set of co-sparse unit-rank estimation (CURE) problems, and establish the statistical underpinnings of these commonly-adopted and yet poorly understood deflation methods.
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