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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
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Related topics

Related topics

23 relations
Biconvex optimizationBiplotData compressionFactor analysis
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Broader (1)

Numerical linear algebra

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
Low Rank Tensor Completion for Multiway Visual Data
Tensor completion recovers missing entries of multiway data. Teh missing of entries could often be caused during teh data… Expand
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Highly Cited
2008
Highly Cited
2008
Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem
There has been continued interest in seeking a theorem describing optimal low-rank approximations to tensors of order 3 or higher… Expand
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Highly Cited
2008
Highly Cited
2008
Improved Nyström low-rank approximation and error analysis
Low-rank matrix approximation is an effective tool in alleviating the memory and computational burdens of kernel methods and… Expand
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Highly Cited
2006
Highly Cited
2006
Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix
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
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Highly Cited
2004
Highly Cited
2004
Generalized low rank approximations of matrices
We consider the problem of computing low rank approximations of matrices. The novelty of our approach is that the low rank… Expand
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Highly Cited
2004
Highly Cited
2004
Fast monte-carlo algorithms for finding low-rank approximations
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
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Highly Cited
2003
Highly Cited
2003
Weighted Low-Rank Approximations
We study the common problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM… Expand
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Highly Cited
2003
Highly Cited
2003
Adaptive Low-Rank Approximation of Collocation Matrices
This article deals with the solution of integral equations using collocation methods with almost linear complexity. Methods such… Expand
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Highly Cited
2003
Highly Cited
2003
Structured low rank approximation
This paper concerns the construction of a structured low rank matrix that is nearest to a given matrix. The notion of structured… Expand
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Highly Cited
2001
Highly Cited
2001
Rank-One Approximation to High Order Tensors
The singular value decomposition (SVD) has been extensively used in engineering and statistical applications. This method was… Expand
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