5 papers
Superfast iterative refinement of low rank approximation of a matrix based on ALS method and random sampling
Qi Luan, Victor Y. Pan
A matrix algorithm runs superfast (aka at sublinear cost) if it involves much fewer flops and memory cells than an input matrix has entries. Big Data are frequently represented by…
Superfast Low Rank Approximation
Soo Go, Qi Luan, Victor Y. Pan +2
Low rank approximation of a matrix (LRA) is a highly important area of Numerical Linear and Multilinear Algebra and Data Mining and Analysis. One can operate with an LRA superfast…
Low Rank Approximation at Sublinear Cost
Qi Luan, Victor Y. Pan, John Svadlenka +1
Low Rank Approximation (LRA) of a matrix is a hot research subject, fundamental for Matrix and Tensor Computations and Big Data Mining and Analysis. Computations with low rank matr…
CUR Low Rank Approximation of a Matrix at Sublinear Cost
Soo Go, Qi Luan, Victor Y. Pan +2
Low rank approximation of a matrix (hereafter LRA) is a highly important area of Numerical Linear and Multilinear Algebra and Data Mining and Analysis. One can operate with an LRA…
Iterative Refinement and Oversampling for Low Rank Approximation
Victor Y. Pan, Qi Luan, Soo Go
Iterative refinement is particularly popular for numerical solution of linear systems of equations. We extend it to Low Rank Approximation of a matrix (LRA) and observe close link…