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Adaptive Estimation in Two-way Sparse Reduced-rank Regression
[article]
2016
arXiv
pre-print
This paper studies the problem of estimating a large coefficient matrix in a multiple response linear regression model when the coefficient matrix could be both of low rank and sparse in the sense that most nonzero entries concentrate on a few rows and columns. We are especially interested in the high dimensional settings where the number of predictors and/or response variables can be much larger than the number of observations. We propose a new estimation scheme, which achieves competitive
arXiv:1403.1922v2
fatcat:qnoeaofavbbktobbuahrt6hf6a