total-least-squares approximation
Application of structured total least squares for system identification and model reduction
... ever, is a difficult nonconvex optimization problem that requires iterative methods. Two methods are proposed in the frame- work of the GTLS problem. In [14], an alternating least squares method is used. ...
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On the equivalence between Total Least Squares and Maximum Likelihood PCA
... Another EIV approach to multivariate calibration and mul- tivariate regression, two kernel problems in analytical chem- istry, was developed by Wentzell et al. [28], who general- ized the PCA method to maximum likelihood ...
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An Example of Peak Finding in Univariate Data by Least Squares Approximation and Restrictions on the Signs of the First Differences
... In order to illustrate the efficacy of our method for identifying important extrema in noisy data, we present a numerical example which considers 31959 data points that span the period from August 1927 to February 2015 ...
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Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares
... Figure 8. Simulations of the IHS algorithm for nuclear-norm constrained problems. The blue curves correspond to the solution of the original (unsketched problem), whereas red curves correspond to the IHS method applied ...
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Consistency of the structured total least squares estimator in a multivariate errors in variables model
... . This modification simplifies the analysis. In Section 6, we study the properties of the inverse weight matrix − 1 . We establish exponential decay of the elements of − 1 , away from the main diagonal. This property is ...
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High performance numerical algorithms and software for structured total least squares
... Lp approximation), research communities (ICCoS, ANMMM); o IWT: PhD Grants; Belgian Federal Science Policy Office IUAP P5/22 (‘Dynamical Systems and Control: Computation, Identification and Modelling’); EU: PDT-COIL, ...
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Block Toeplitz/Hankel structured total least squares
... 6.1. Improvement of the subspace identification estimate. Maximum likelihood SISO transfer function identification from noisy input/output data can be formulated as an STLS problem with a data matrix composed of two Hankel ...
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The element wise weighted total least squares problem
... The EW-TLS estimatorgeneralizes the TLS estimatorand improves its statistical ac- curacy under more general noise assumptions, but makes the problem computationally more difficult. Indeed, while the TLS problem has a ...
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Overview of total least squares methods
... classical total least squares method and describe algorithms for its generalization to weighted and structured approximation ...classical total least squares problem has a ...
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On weighted structured total least squares
... is a solution technique for an overdetermined system of equations AX ≈ B, A ∈ IR m × n , B ∈ IR m × d . It is a natural generalization of the least squares approximation method when the data in both ...
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Application of structured total least squares for system identification and model reduction
... subid is designed for ARMAX system identification. detss is designed for exact system identification. Both they are applied heuristically to a situation where the assumptions under which they are derived are not ...
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On the computation of the structured total least squares estimator
... Dr S. Van Huel is a full professor and I. Markovsky is a research assistant at the Katholieke Universiteit Leuven, Belgium. I. Markovsky is supported by a K.U. Leuven doctoral scholarship. Dr A. Kukush is supported by a ...
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An adapted version of the element wise weighted total least squares method for applications in chemometrics
... Weighted Total Least Squares (EW-TLS) [4,5] method can be reduced to the same mathematical problem, ...matrix approximation where the weight is derived from the distribution of the measurement ...
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Peak Estimation of a Spectrum from Noisy Measurements by Least Squares Piecewise Monotonic Data Approximation
... monotonic approximation as a data smoothing approach can have many ...monotonic approximation to peak estimation of spectra that are represented by some noisy measurements of their ...
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Computing Approximation GCD of Several Polynomials by Structured Total Least Norm
... The task of determining the greatest common divisors (GCD) for several polynomials which arises in image compres- sion, computer algebra and speech encoding can be formulated as a low rank approximation problem ...
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On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... The paper establishes the conditions under which the generalised least squares estima- tor of the regression parameters is equivalent to the weighted least squares estimator. The equivalence ...
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Approximate Least Squares Accelerator
... simplest approximation (the truncation of inputs) it is a difficult task to find the optimal truncation configura- tion which minimizes the energy ...each approximation, the unit gate model is ...an ...
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Least-Squares Policy Iteration
... value-function approximation with linear architectures and approximate policy iter- ...the least-squares temporal-difference learning algorithm (LSTD) for prediction problems, which is known for its ...
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Floating point error analysis of recursive least squares and least means squares adaptive filters
... This sequence is a zero mean white independent random process which has a variance related to signal statistics, the weight vector covariance, and the floating point errorso The calculat[r] ...
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Stabilized Least Squares Migration
... The goal of this research is to determine whether or not stabilizing a least squares migration (LSM) is possible using velocity model updates. Traditionally LSM is an unstable process which breaks down in ...
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