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Semantic Scholar uses AI to extract papers important to this topic.

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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