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 ...

The singular value decomposition is widely used to approximate data matrices with lower rank matrices. Feng and He [Ann. Appl. Stat. 3 (2009) 1634-1654] developed tests on dimensionality of the mean ...

Structured low-rank approximation (SLRA) is a mathematical framework that seeks to approximate a given data matrix by another matrix of lower rank while preserving intrinsic structural properties.

Reduced rank approximation of matrices has hitherto been possible only by unweighted least squares. This paper presents iterative techniques for obtaining such approximations when weights are ...