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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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Papers overview

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Review
2019
Review
2019
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
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
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
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
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
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
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
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
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
The singular value decomposition (SVD) has been extensively used in engineering and statistical applications. This method was… Expand
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