# Linear least squares (mathematics)

## Papers overview

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Highly Cited

2013

Highly Cited

2013

- NeuroImage
- 2013

PURPOSE
Linear least squares estimators are widely used in diffusion MRI for the estimation of diffusion parameters. Althoughâ€¦Â (More)

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Highly Cited

2011

Highly Cited

2011

- 2011

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether theyâ€¦Â (More)

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Highly Cited

2011

Highly Cited

2011

- IEEE Transactions on Geoscience and Remoteâ€¦
- 2011

We present a new algorithm for linear spectral mixture analysis, which is capable of supervised unmixing of hyperspectral dataâ€¦Â (More)

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Highly Cited

2009

Highly Cited

2009

- 2009

The affine rank minimization problem, which consists of finding a matrix of minimum rank subject to linear equality constraintsâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- 2007

This work addresses the problem of regularized linear least squares (RLS) with non-quadratic separable regularization. Despiteâ€¦Â (More)

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Highly Cited

2006

Highly Cited

2006

- Bioinformatics
- 2006

MOTIVATION
Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital imagesâ€¦Â (More)

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Highly Cited

2001

Highly Cited

2001

- IEEE Trans. Geoscience and Remote Sensing
- 2001

Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materialsâ€¦Â (More)

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Highly Cited

1997

Highly Cited

1997

- 1997

In this paper a modification of the standard algorithm for non-negativity-constrained linear least squares regression is proposedâ€¦Â (More)

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Highly Cited

1997

Highly Cited

1997

- SIAM J. Scientific Computing
- 1997

The total least squares (TLS) method is a successful method for noise reduction in linear least squares problems in a number ofâ€¦Â (More)

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Highly Cited

1996

Highly Cited

1996

- Machine Learning
- 1996

We introduce two new temporal difference (TD) algorithms based on the theory of linear least-squares function approximation. Weâ€¦Â (More)

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