Results 261 to 270 of about 1,010,511 (312)

Multipath least squares algorithm and analysis

Signal Processing, 2020
Abstract One important task in signal processing is to construct effective algorithms to reconstruct sparse signals from an underdetermined system of linear equations. In this paper, we propose a new sparse recovery algorithm called multipath least squares (MLS), which investigates multiple promising candidates per step and parallels the multipath ...
Pengbo Geng, Jian Wang 0016, Wengu Chen
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Perturbation analysis for mixed least squares–total least squares problems

Numerical Linear Algebra with Applications, 2019
SummaryIn many linear parameter estimation problems, one can use the mixed least squares–total least squares (MTLS) approach to solve them. This paper is devoted to the perturbation analysis of the MTLS problem. Firstly, we present the normwise, mixed, and componentwise condition numbers of the MTLS problem, and find that the normwise, mixed, and ...
Bing Zheng, Zhanshan Yang
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Perturbation Analysis of Orthogonal Least Squares

Canadian Mathematical Bulletin, 2019
AbstractThe Orthogonal Least Squares (OLS) algorithm is an efficient sparse recovery algorithm that has received much attention in recent years. On one hand, this paper considers that the OLS algorithm recovers the supports of sparse signals in the noisy case. We show that the OLS algorithm exactly recovers the support of $K$-sparse signal $\boldsymbol{
Geng, Pengbo, Chen, Wengu, Ge, Huanmin
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Least-squares analysis of the Mueller matrix

Optics Letters, 2006
In a single-mode fiber excited by light with a fixed polarization state, the output polarizations obtained at two different optical frequencies are related by a Mueller matrix. We examine least-squares procedures for estimating this matrix from repeated measurements of the output Stokes vector for a random set of input polarization states.
Michael, Reimer, David, Yevick
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An Analysis of the Total Least Squares Problem

SIAM Journal on Numerical Analysis, 1980
Totla least squares (TLS) is a method of fitting that is appropriate when there are errors in both the observation vector $b (mxl)$ and in the data matrix $A (mxn)$. The technique has been discussed by several authors and amounts to fitting a "best" subspace to the points $(a^{T}_{i},b_{i}), i=1,\ldots,m,$ where $a^{T}_{i}$ is the $i$-th row of $A$. In
Golub, Gene H., Van Loan, Charles F.
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Least square methods in structural analysis

Computers & Structures, 2002
A new approach to structural analysis is presented. The method uses equilibrium and deformation geometry field relations directly without resorting to an energy formulation. A matrix vector of errors in the field relations is minimized first with respect to the internal quantities, e.g., the moments.
L. Selna, A. Hakam
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Unified Least Squares Analysis

Journal of the American Statistical Association, 1965
Abstract It is seen (through consideration of generalized inverses) that the abbreviated Doolittle method serves, in problems of linear estimation, as a solution technique for models of less than full rank as well as for models of full rank, and in identically the same fashion.
C. A. Rohde, J. R. Harvey
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Least Squares Methods of Analysis

1983
In the first two contributions we have discussed the collection of single-photon decay data, which has been assumed undistorted by the excitation pulse — the assumption of a delta-pulse excitation. However, for decay times comparable in time-length to the excitation pulse this assumption is untrue, and we have an experimental result which is a ...
B. K. Selinger, C. M. Harris, A. J. Kallir   +2 more
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Least squares linear discriminant analysis

Proceedings of the 24th international conference on Machine learning, 2007
Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction and classification. LDA in the binaryclass case has been shown to be equivalent to linear regression with the class label as the output. This implies that LDA for binary-class classifications can be formulated as a least squares problem.
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Perturbation analysis and condition numbers of mixed least squares-scaled total least squares problem

Numerical Algorithms, 2021
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Pingping Zhang, Qun Wang
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