Skip to search formSkip to main content
You are currently offline. Some features of the site may not work correctly.

Low-rank approximation

Known as: Low rank approximation 
In mathematics, low-rank approximation is a minimization problem, in which the cost function measures the fit between a given matrix (the data) and… Expand
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2008
Highly Cited
2008
There has been continued interest in seeking a theorem describing optimal low-rank approximations to tensors of order 3 or higher… Expand
  • table 7.1
Highly Cited
2008
Highly Cited
2008
Low-rank matrix approximation is an effective tool in alleviating the memory and computational burdens of kernel methods and… Expand
  • table 1
  • table 2
  • figure 2
  • figure 1
  • table 3
Highly Cited
2006
Highly Cited
2006
In many applications, the data consist of (or may be naturally formulated as) an $m \times n$ matrix $A$. It is often of interest… Expand
  • table 1
  • figure 2
  • figure 4
Highly Cited
2004
Highly Cited
2004
We consider the problem of computing low rank approximations of matrices. The novelty of our approach is that the low rank… Expand
  • table I
  • table II
  • table III
  • table IV
  • table V
Highly Cited
2004
Highly Cited
2004
We consider the problem of approximating a given <i>m</i> × <i>n</i> matrix <b>A</b> by another matrix of specified rank <i>k</i… Expand
Highly Cited
2003
Highly Cited
2003
We study the common problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM… Expand
  • figure 1
Highly Cited
2003
Highly Cited
2003
This article deals with the solution of integral equations using collocation methods with almost linear complexity. Methods such… Expand
  • figure 1
  • figure 2
  • figure 3
  • table 1
  • table 2
Highly Cited
2003
Highly Cited
2003
This paper concerns the construction of a structured low rank matrix that is nearest to a given matrix. The notion of structured… Expand
  • figure 1
  • figure 2
  • figure 3
  • table 1
  • table 3
Highly Cited
2001
Highly Cited
2001
The singular value decomposition (SVD) has been extensively used in engineering and statistical applications. This method was… Expand
Highly Cited
2000
Highly Cited
2000
Summary. This article considers the problem of approximating a general asymptotically smooth function in two variables, typically… Expand