Low-rank approximation and dimensionality reduction techniques form the backbone of modern computational methods by enabling the efficient representation of large and high‐dimensional datasets. These ...

In high-dimensional multivariate regression problems, enforcing low rank in the coefficient matrix offers effective dimension reduction, which greatly facilitates parameter estimation and model ...

This study examines 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 ...