Linear least squares (mathematics)

Known as: Constrained regression, Constrained linear least squares, Normal equations 
In statistics and mathematics, linear least squares is an approach fitting a mathematical or statistical model to data in cases where the idealized… (More)
Wikipedia

Related topics

Related topics

49 relations
B-splineBundle adjustmentChemometricsCholesky decomposition
(More)

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2013
Highly Cited
2013
Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls
PURPOSE Linear least squares estimators are widely used in diffusion MRI for the estimation of diffusion parameters. Although… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2011
Highly Cited
2011
Robust linear least squares regression
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they… (More)
  • table 1
  • table 2
  • table 3
  • table 4
  • table 5
Is this relevant?
Highly Cited
2011
Highly Cited
2011
Fully Constrained Least Squares Spectral Unmixing by Simplex Projection
We present a new algorithm for linear spectral mixture analysis, which is capable of supervised unmixing of hyperspectral data… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2009
Highly Cited
2009
An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems
The affine rank minimization problem, which consists of finding a matrix of minimum rank subject to linear equality constraints… (More)
  • table 1
  • table 2
  • table 3
  • table 3
  • table 4
Is this relevant?
Highly Cited
2007
Highly Cited
2007
Coordinate and subspace optimization methods for linear least squares with non-quadratic regularization
This work addresses the problem of regularized linear least squares (RLS) with non-quadratic separable regularization. Despite… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2006
Highly Cited
2006
Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis
MOTIVATION Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images… (More)
  • table 1
  • table 2
  • figure 1
  • figure 2
  • table 3
Is this relevant?
Highly Cited
2001
Highly Cited
2001
Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery
Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
1997
Highly Cited
1997
A FAST NON-NEGATIVITY-CONSTRAINED LEAST SQUARES ALGORITHM
In this paper a modification of the standard algorithm for non-negativity-constrained linear least squares regression is proposed… (More)
  • table 1
  • table 2
Is this relevant?
Highly Cited
1997
Highly Cited
1997
Regularization by Truncated Total Least Squares
The total least squares (TLS) method is a successful method for noise reduction in linear least squares problems in a number of… (More)
  • figure 1
  • figure 2
  • table 1
Is this relevant?
Highly Cited
1996
Highly Cited
1996
Linear Least-Squares Algorithms for Temporal Difference Learning
We introduce two new temporal difference (TD) algorithms based on the theory of linear least-squares function approximation. We… (More)
Is this relevant?