Results 221 to 230 of about 553,645 (265)

Least-squares meshes

Proceedings Shape Modeling Applications, 2004., 2004
In this paper we introduce least-squares meshes: meshes with a prescribed connectivity that approximate a set of control points in a least-squares sense. The given mesh consists of a planar graph with arbitrary connectivity and a sparse set of control points with geometry. The geometry of the mesh is reconstructed by solving a sparse linear system. The
Olga Sorkine, Daniel Cohen-Or
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Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression

2012
Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table.
Hervé, Abdi, Lynne J, Williams
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The Inefficiency of Least Squares

Biometrika, 1975
SUMMARY Two criteria are set up to judge the relative performance of the least squares estimator and the best linear unbiased estimator of , in the linear model y = X/, + u, where E(u) = 0, E(uu') = F. The matrices X and r are found so that the relative performance of least squares is worst.
Bloomfield, Peter, Watson, Geoffrey S.
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The Method of Least Squares

2016
The English version of this paper appeared two years after the Chinese “original”. During the 1950s and early 1960s, DDK visited China several times on exchange programmes.
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Fuzzy least squares

Information Sciences, 1988
The author discusses three models of fuzzy linear regression function for triangular fuzzy numbers [Fuzzy numbers with triangular shapes, cf. \textit{D. Dubois} and \textit{H. Prade}, Int. J. Syst. Sci. 9, 613-626 (1978; Zbl 0383.94045)]. Formulas are deduced by the least-squares method.
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Constrained least-squares

Computers & Graphics, 1978
Abstract The method of least-squares is intended to fit a function to a set of data points closely, so as to satisfy a particular criterion of closeness. The approximating function can be taken to be a linear combination of linearly independent functions of an independent variable.
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Least squares and total least squares methods in image restoration

1997
Image restoration is the process of removing or minimizing degradations (blur) in an image. Mathematically, it can be modeled as a discrete ill-posed problem Hf=g, where H is a matrix of large dimension representing the blurring phenomena, and g is a vector representing the observed image.
Julie Kamm, James G. Nagy
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Least Squares or Least Circles?

CHANCE, 2010
(2010). Least Squares or Least Circles? CHANCE: Vol. 23, Collecting Data in Challenging Settings, pp. 38-42.
Ivo Petras, Igor Podlubny
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Maximum likelihood, least squares, and penalized least squares for PET

IEEE Transactions on Medical Imaging, 1993
The EM algorithm is the basic approach used to maximize the log likelihood objective function for the reconstruction problem in positron emission tomography (PET). The EM algorithm is a scaled steepest ascent algorithm that elegantly handles the nonnegativity constraints of the problem. It is shown that the same scaled steepest descent algorithm can be
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Collinearity and Total Least Squares

SIAM Journal on Matrix Analysis and Applications, 1994
The least squares (LS) and total least squares (TLS) methods are commonly used to solve the overdetermined system of equations \(Ax\approx b\). The main objective of this paper is to examine TLS when \(A\) is nearly rank deficient by outlining its differences and similarities to the well-known truncated LS method.
Ricardo D. Fierro, James R. Bunch
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