# Singular value decomposition

## Papers overview

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Highly Cited

2010

Highly Cited

2010

- SIAM J. Matrix Analysis Applications
- 2010

We define the hierarchical singular value decomposition (SVD) for tensors of order d â‰¥ 2. This hierarchical SVD has propertiesâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- 2007

The regularized SVD model has O(NK + MK) parameters (N users, M movies, K features). We propose two models with O(MK) parametersâ€¦Â (More)

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Highly Cited

2005

Highly Cited

2005

- 2005

We study the SVD of an arbitrary matrix , especially its subspaces of activation, which leads in natural manner to pseudoinverseâ€¦Â (More)

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Highly Cited

2004

Highly Cited

2004

- Machine Learning
- 2004

We consider the problem of partitioning a set of m points in the n-dimensional Euclidean space into k clusters (usually m and nâ€¦Â (More)

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Highly Cited

2002

Highly Cited

2002

- 2002

This chapter describes gene expression analysis by Singular Value Decomposition (SVD), emphasizing initial characterization ofâ€¦Â (More)

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Highly Cited

2002

Highly Cited

2002

- ECCV
- 2002

We introduce an incremental singular value decomposition ( SVD) of incomplete data. The SVD is developed as data arrives, and canâ€¦Â (More)

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Highly Cited

2000

Highly Cited

2000

- SIAM J. Matrix Analysis Applications
- 2000

We discuss a multilinear generalization of the singular value decomposition. There is a strong analogy between several propertiesâ€¦Â (More)

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Highly Cited

1996

Highly Cited

1996

- IEEE Trans. Communications
- 1996

A new approach to low-complexity channel estimation in orthogonal-frequency division multiplexing (OFDM) systems is proposed. Aâ€¦Â (More)

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Highly Cited

1995

Highly Cited

1995

- ISSAC
- 1995

This paper introduces singular value decomposition (SVD) algorithms for some standard polynomial computations, in the case whereâ€¦Â (More)

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Highly Cited

1993

Highly Cited

1993

- 1993

In this paper, we present a unified approach to the (related) problems of recovering signal parameters from noisy observationsâ€¦Â (More)

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