Skip to search formSkip to main contentSkip to account menuSemantic Scholar Semantic Scholar's Logo Search 207,868,567 papers from all fields of science

Search

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

Highly Cited

2012

Highly Cited

2012

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

Data Approximation by Low-complexity Models details the theory, algorithms, and applications of structured low-rank approximation…

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…

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…

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…

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…

Highly Cited

2003

Highly Cited

2003

This article deals with the solution of integral equations using collocation methods with almost linear complexity. Methods such…

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…

Highly Cited

2001

Highly Cited

2001

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

Highly Cited

2001

Highly Cited

2001

Given a matrix <italic>A</italic> it is often desirable to find an approximation to <italic>A</italic> that has low rank. We…