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Found 2 papers, 1 papers with code
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.
Boosted Sparse and Low-Rank Tensor Regression
We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse.
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