Add to ORKGCUR matrix decomposition is a randomized algorithm that can efficiently compute the low rank approximation for a given rectangle matrix (Drineas et al., 2006, Mahoney and Drineas, 2008, 2009). Let A ∈ Rn×m be the given matrix and k be the target rank for approximation. CUR randomly samples c = O(k log k/2) columns and r = O(k log k/2) rows from A, according to their leverag
Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computin...
In this paper we present an algorithm for computing a low rank approximation of a sparse matrix base...
We present a fast randomized algorithm that computes a low rank LU decomposition. Our algorithm uses...
CUR matrix decomposition computes the low rank approximation of a given matrix by using the actual r...
“study [low-rank] matrix approximations that are explicitly expressed in terms of a small numbers of...
The CUR matrix decomposition and the Nyström approximation are two important low-rank matrix approx...
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and ...
Principal components analysis and, more generally, the Singular Value Decomposition are fundamental ...
Low-rank approximations which are computed from selected rows and columns of a given data matrix hav...
We derive a CUR matrix factorization based on the Discrete Empirical Interpolation Method (DEIM). Fo...
International audiencen this paper we present an algorithm for computing a low rank approximation of...
We derive a CUR matrix factorization based on the Discrete Empirical Interpolation Method (DEIM). Fo...
The manuscript describes efficient algorithms for the computation of the CUR and ID decompositions. ...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computin...
In this paper we present an algorithm for computing a low rank approximation of a sparse matrix base...
We present a fast randomized algorithm that computes a low rank LU decomposition. Our algorithm uses...
CUR matrix decomposition computes the low rank approximation of a given matrix by using the actual r...
“study [low-rank] matrix approximations that are explicitly expressed in terms of a small numbers of...
The CUR matrix decomposition and the Nyström approximation are two important low-rank matrix approx...
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and ...
Principal components analysis and, more generally, the Singular Value Decomposition are fundamental ...
Low-rank approximations which are computed from selected rows and columns of a given data matrix hav...
We derive a CUR matrix factorization based on the Discrete Empirical Interpolation Method (DEIM). Fo...
International audiencen this paper we present an algorithm for computing a low rank approximation of...
We derive a CUR matrix factorization based on the Discrete Empirical Interpolation Method (DEIM). Fo...
The manuscript describes efficient algorithms for the computation of the CUR and ID decompositions. ...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computin...
In this paper we present an algorithm for computing a low rank approximation of a sparse matrix base...
We present a fast randomized algorithm that computes a low rank LU decomposition. Our algorithm uses...