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… (More)
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Highly Cited
2015
Highly Cited
2015
We show how to approximate a data matrix A with a much smaller sketch ~A that can be used to solve a general class of constrained… (More)
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Highly Cited
2012
Highly Cited
2012
If you really want to be smarter, reading can be one of the lots ways to evoke and realize. Many people who like reading will… (More)
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Highly Cited
2008
Highly Cited
2008
Fitting data by a bounded complexity linear model is equivalent to low-rank approximation of a matrix constructed from the data… (More)
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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… (More)
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Highly Cited
2007
Highly Cited
2007
For the low rank approximation of time-dependent data matrices and of solutions to matrix differential equations, an increment… (More)
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Highly Cited
2007
Highly Cited
2007
We describe two recently proposed randomized algorithms for the construction of low-rank approximations to matrices, and… (More)
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Highly Cited
2005
Highly Cited
2005
The problem of computing low rank approximations of matrices is considered. The novel aspect of our approach is that the low rank… (More)
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Highly Cited
2003
Highly Cited
2003
Weighted-norms can arise in several situations. A zero/one weighted-norm, for example, arises when some of the entries in the… (More)
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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… (More)
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Highly Cited
2002
Highly Cited
2002
This paper concerns the construction of a structured low rank matrix that is nearest to a given matrix. The notion of structured… (More)
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