Add to ORKGWeighted low-rank approximation (WLRA), a dimensionality reduction technique for data analysis, has been successfully used in several applications, such as in collaborative filtering to design recommender systems or in computer vision to recover structure from motion. In this paper, we study the computational complexity of WLRA and prove that it is NP-hard to find an approximate solution, even when a rank-one approximation is sought. Our proofs are based on a reduction from the maximum-edge biclique problem, and apply to strictly positive weights as well as binary weights (the latter corresponding to low-rank matrix approximation with missing data)
Low-rank binary matrix approximation is a generic problem where one seeks a good approximation of a ...
The problem of finding a low rank approximation of a given measurement matrix is of key interest in ...
It is common in recommender systems rating matrix, where the input ma-trix R is bounded in between [...
Weighted low-rank approximation (WLRA), a dimensionality reduction technique for data analysis, has ...
The calculation of a low-rank approximation to a matrix is fundamental to many algorithms in compute...
The calculation of a low-rank approximation to a matrix is fundamental to many algorithms in compute...
We study the common problem of approximating a target matrix with a matrix of lower rank. We provi...
Rank deficiency of a data matrix is equivalent to the existence of an exact linear model for the dat...
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer ...
Linear models identification from data with missing values is posed as a weighted low-rank approxima...
Abstract. We consider low-rank approximation of affinely structured matrices with missing elements. ...
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer ...
Rank deficiency of a data matrix is equivalent to the existence of an exact linear model for the dat...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer ...
Low-rank binary matrix approximation is a generic problem where one seeks a good approximation of a ...
The problem of finding a low rank approximation of a given measurement matrix is of key interest in ...
It is common in recommender systems rating matrix, where the input ma-trix R is bounded in between [...
Weighted low-rank approximation (WLRA), a dimensionality reduction technique for data analysis, has ...
The calculation of a low-rank approximation to a matrix is fundamental to many algorithms in compute...
The calculation of a low-rank approximation to a matrix is fundamental to many algorithms in compute...
We study the common problem of approximating a target matrix with a matrix of lower rank. We provi...
Rank deficiency of a data matrix is equivalent to the existence of an exact linear model for the dat...
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer ...
Linear models identification from data with missing values is posed as a weighted low-rank approxima...
Abstract. We consider low-rank approximation of affinely structured matrices with missing elements. ...
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer ...
Rank deficiency of a data matrix is equivalent to the existence of an exact linear model for the dat...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
The calculation of a low-rank approximation of a matrix is a fundamental operation in many computer ...
Low-rank binary matrix approximation is a generic problem where one seeks a good approximation of a ...
The problem of finding a low rank approximation of a given measurement matrix is of key interest in ...
It is common in recommender systems rating matrix, where the input ma-trix R is bounded in between [...