: As an extension to 2DPCA, Generalized Low Rank Approximation of Matrices (GLRAM) applies two-sided (i.e., the left and right) rather than single-sided (i.e., the left or the righ...
Recently four non-iterative algorithms for simultaneous low rank approximations of matrices (SLRAM) have been presented by several researchers. In this paper, we show that those a...
: Covariance matrices capture correlations that are invaluable in modeling real-life datasets. Using all d2 elements of the covariance (in d dimensions) is costly and could result ...
Abstract--Low-rank matrix approximation has applications in many fields, such as 2D filter design and 3D reconstruction from an image sequence. In this paper, one issue with low-ra...
We prove that any real matrix A contains a subset of at most 4k/ + 2k log(k + 1) rows whose span "contains" a matrix of rank at most k with error only (1 + ) times the er...