Results 1 to 10 of about 1,995,456 (118)
Approximate Least Squares [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013 We present a novel iterative algorithm for approximating the linear least squares solution with low complexity. After a motivation of the algorithm we discuss the algorithm's properties including its complexity, and we present theoretical results as well
Huemer, Mario +2 more
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, 2000 Least trimmed squares (LTS) is a statistical technique for estimation of unknown parameters of a linear regression model and provides a “robust” alternative to the classical regression method based on minimizing the sum of squared residuals.
Víšek, Jan Ámos, Čížek, Pavel
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Partitioned least squares [PDF]
Machine Learning, 2019 AbstractLinear least squares is one of the most widely used regression methods in many fields. The simplicity of the model allows this method to be used when data is scarce and allows practitioners to gather some insight into the problem by inspecting the values of the learnt parameters.
Roberto Esposito +2 more
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Unifying Least Squares, Total Least Squares and Data Least Squares [PDF]
, 2002 The standard approaches to solving overdetermined linear systems A x ≈ b construct minimal corrections to the vector b and/or the matrix A such that the corrected system is compatible. In ordinary least squares (LS) the correction is restricted to b, while in data least squares (DLS) it is restricted to A. In scaled total least squares (Scaled TLS) [15]
Christopher C. Paige, Zdeněk Strakoš
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Least-squares inversion for density-matrix reconstruction [PDF]
, 1997 We propose a method for reconstruction of the density matrix from measurable time-dependent (probability) distributions of physical quantities. The applicability of the method based on least-squares inversion is - compared with other methods - very ...
A. Zucchetti +48 more
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Geodesic least squares regression on information manifolds [PDF]
, 2014 We present a novel regression method targeted at situations with significant uncertainty on both the dependent and independent variables or with non-Gaussian distribution models.
Verdoolaege, Geert
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Subspace Least Squares Multidimensional Scaling
, 2017 Multidimensional Scaling (MDS) is one of the most popular methods for dimensionality reduction and visualization of high dimensional data. Apart from these tasks, it also found applications in the field of geometry processing for the analysis and ...
Boyarski, Amit +2 more
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Least Squares Two-Point Function Estimation
, 2018 The standard estimator for the two-point function of a homogeneous and isotropic random field is a special case of a larger class of least squares estimators that interpolate the function values. Using a different interpolation scheme, two-point function
Tessore, Nicolas
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On weighted structured total least squares
, 2006 In this contribution we extend the result of (Markovsky et. al, SIAM J. of Matrix Anal. and Appl., 2005) to the case of weighted cost function. It is shown that the computational complexity of the proposed algorithm is preserved linear in the sample size
G. Golub +4 more
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Bayesian least squares deconvolution
, 2015 Aims. To develop a fully Bayesian least squares deconvolution (LSD) that can be applied to the reliable detection of magnetic signals in noise-limited stellar spectropolarimetric observations using multiline techniques. Methods. We consider LSD under the
Petit, P., Ramos, A. Asensio
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