6 citations · 22 across the 8 of their papers we have counts for
4 papers · 1 filter
CUR Low Rank Approximation at Deterministic Sublinear Cost
Qi Luan, Victor Y. Pan, John Svadlenka
A matrix algorithm runs at {\em sublinear cost} if it uses much fewer memory cells and arithmetic operations than the input matrix has entries. Such algorithms are indispensable fo…
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…
QRP Variation of Cross--Approximation Iterations for Low Rank Approximation
Victor Y. Pan, John Svadlenka
We call matrix algorithms superfast if they use much fewer flops and memory cells than the input matrix has entries. Using such algorithms is indispensable for Big Data Mining and…