Add to ORKGThis paper describes a suite of algorithms for constructing low-rank approximations of an input matrix from a random linear image, or sketch, of the matrix. These methods can preserve structural properties of the input matrix, such as positive-semidefiniteness, and they can produce approximations with a user-specified rank. The algorithms are simple, accurate, numerically stable, and provably correct. Moreover, each method is accompanied by an informative error bound that allows users to select parameters a priori to achieve a given approximation quality. These claims are supported by numerical experiments with real and synthetic data
A common approach for compressing large-scale data is through matrix sketching. In this work, we con...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-reve...
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...
This paper describes a suite of algorithms for constructing low-rank approximations of an input matr...
This paper develops a suite of algorithms for constructing low-rank approximations of an input matri...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
It is often desirable to reduce the dimensionality of a large dataset by projecting it onto a low-di...
A methodology for using random sketching in the context of model order reduction for high-dimensiona...
We study matrix sketching methods for regularized variants of linear regression, low rank approximat...
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a give...
A common approach for compressing large-scale data is through matrix sketching. In this work, we con...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
Matrix sketching is a recently developed data compression technique. An input matrix A is efficient...
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the e...
This Julia package provides fast low-rank approximation algorithms for BLAS/LAPACK-compatible matric...
A common approach for compressing large-scale data is through matrix sketching. In this work, we con...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-reve...
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...
This paper describes a suite of algorithms for constructing low-rank approximations of an input matr...
This paper develops a suite of algorithms for constructing low-rank approximations of an input matri...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
It is often desirable to reduce the dimensionality of a large dataset by projecting it onto a low-di...
A methodology for using random sketching in the context of model order reduction for high-dimensiona...
We study matrix sketching methods for regularized variants of linear regression, low rank approximat...
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a give...
A common approach for compressing large-scale data is through matrix sketching. In this work, we con...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
Matrix sketching is a recently developed data compression technique. An input matrix A is efficient...
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the e...
This Julia package provides fast low-rank approximation algorithms for BLAS/LAPACK-compatible matric...
A common approach for compressing large-scale data is through matrix sketching. In this work, we con...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-reve...
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...