Add to ORKGWe consider ₁-Rank-r Approximation over {GF}(2), where for a binary m× n matrix and a positive integer constant r, one seeks a binary matrix of rank at most r, minimizing the column-sum norm ‖ -‖₁. We show that for every ε ∈ (0, 1), there is a {randomized} (1+ε)-approximation algorithm for ₁-Rank-r Approximation over {GF}(2) of running time m^{O(1)}n^{O(2^{4r}⋅ ε^{-4})}. This is the first polynomial time approximation scheme (PTAS) for this problem
We prove that any real matrix A contains a subset of at most 4k/ɛ+2k log(k+1) rows whose span “conta...
A problem for many kernel-based methods is that the amount of computation required to find the solut...
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the e...
We consider ℓ1-Rank-r Approximation over GF(2), where for a binary m × n matrix A and a positive int...
We consider ₁-Rank-r Approximation over {GF}(2), where for a binary m× n matrix and a positive inte...
We provide a randomized linear time approximation scheme for a generic problem about clustering of b...
Low-rank binary matrix approximation is a generic problem where one seeks a good approximation of a ...
In this paper, we address the problem of obtaining low-rank approximations that are directly express...
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a give...
We prove that for any real-valued matrix X ∈ Rm×n, and positive integers r> k, there is a subset ...
We provide a number of algorithmic results for the following family of problems: For a given binary ...
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...
In this paper, we revisit the problem of constructing a near-optimal rank k approximation of a matri...
For a given matrix H which has d singular values larger than ε, an expression for all rank-d approxi...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
We prove that any real matrix A contains a subset of at most 4k/ɛ+2k log(k+1) rows whose span “conta...
A problem for many kernel-based methods is that the amount of computation required to find the solut...
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the e...
We consider ℓ1-Rank-r Approximation over GF(2), where for a binary m × n matrix A and a positive int...
We consider ₁-Rank-r Approximation over {GF}(2), where for a binary m× n matrix and a positive inte...
We provide a randomized linear time approximation scheme for a generic problem about clustering of b...
Low-rank binary matrix approximation is a generic problem where one seeks a good approximation of a ...
In this paper, we address the problem of obtaining low-rank approximations that are directly express...
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a give...
We prove that for any real-valued matrix X ∈ Rm×n, and positive integers r> k, there is a subset ...
We provide a number of algorithmic results for the following family of problems: For a given binary ...
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...
In this paper, we revisit the problem of constructing a near-optimal rank k approximation of a matri...
For a given matrix H which has d singular values larger than ε, an expression for all rank-d approxi...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
We prove that any real matrix A contains a subset of at most 4k/ɛ+2k log(k+1) rows whose span “conta...
A problem for many kernel-based methods is that the amount of computation required to find the solut...
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the e...