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…
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Papers overview

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Highly Cited
2012
Highly Cited
2012
• ArXiv
• 2012
• Corpus ID: 14094215
We design a new distribution over m × n matrices S so that, for any fixed n × d matrix A of rank r, with probability at least 9…
Highly Cited
2012
Highly Cited
2012
• I. Markovsky
• Communications and Control Engineering
• 2012
• Corpus ID: 46336784
Data Approximation by Low-complexity Models details the theory, algorithms, and applications of structured low-rank approximation…
Highly Cited
2008
Highly Cited
2008
• SIAM Journal on Matrix Analysis and Applications
• 2008
• Corpus ID: 7159193
There has been continued interest in seeking a theorem describing optimal low-rank approximations to tensors of order 3 or higher…
Highly Cited
2008
Highly Cited
2008
• International Conference on Machine Learning
• 2008
• Corpus ID: 10651609
Low-rank matrix approximation is an effective tool in alleviating the memory and computational burdens of kernel methods and…
Highly Cited
2006
Highly Cited
2006
• SIAM journal on computing (Print)
• 2006
• Corpus ID: 5453786
In many applications, the data consist of (or may be naturally formulated as) an $m \times n$ matrix $A$. It is often of interest…
Highly Cited
2005
Highly Cited
2005
• Jieping Ye
• Machine-mediated learning
• 2005
• Corpus ID: 490977
The problem of computing low rank approximations of matrices is considered. The novel aspect of our approach is that the low rank…
Highly Cited
2003
Highly Cited
2003
• Computing
• 2003
• Corpus ID: 16501661
This article deals with the solution of integral equations using collocation methods with almost linear complexity. Methods such…
Highly Cited
2003
Highly Cited
2003
• International Conference on Machine Learning
• 2003
• Corpus ID: 5815325
We study the common problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM…
Highly Cited
2001
Highly Cited
2001
• SIAM Journal on Matrix Analysis and Applications
• 2001
• Corpus ID: 22258700
The singular value decomposition (SVD) has been extensively used in engineering and statistical applications. This method was…
Highly Cited
2001
Highly Cited
2001
• Symposium on the Theory of Computing
• 2001
• Corpus ID: 2683832
Given a matrix <italic>A</italic> it is often desirable to find an approximation to <italic>A</italic> that has low rank. We…