This article proposes a modeling framework for high-dimensional experimental data, such as brain images or microarrays, that discovers statistically significant structures most relevant to the ...

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

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 ...