weighted least-squares algorithm
On weighted structured total least squares
... total least squares problem to the case of weighted cost func- ...proposed algorithm is preserved linear in the sample size when the weight matrix is banded with bandwidth that is independent ...
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Tree Based Orthogonal Least Squares Regression with Repeated Weighted Boosting Search
... In fact, one can implement the tree structure search in many variants of the OLSR algorithm. This paper only considers Ref. [9]. In [9], the optimization at each regression stage is carried out with a simple ...
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Robust Regression Analysis with LR Type Fuzzy Input Variables and Fuzzy Output Variable
... fuzzy weighted least squares method for an outlier condition, making weighted with degree of membership and lean on an interaction with the ...suggest weighted fuzzy at the least ...
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A weighted least squares solution for space intersection of spaceborne stereo SAR data
... Abstract—The use of stereoscopic SAR images offers an alter- native to interferometric SAR for the generation of digital eleva- tion models (DEMs). The stereo radargrammetric method is ro- bust and can generate DEMs of ...
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Asymptotic properties of weighted least squares estimation in weak parma models
... the least squares (LS) estimators of PARMA models with independent errors; they extended the results for periodic autoregression earlier derived by Pagano (1978) and Troutman ...efficient algorithm ...
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Analysis of Ammonia Nitrogen Content in Water Based on Weighted Least Squares Support Vector Machine (WLSSVM) Algorithm
... This algorithm is based on the learning theory of limited sample, which is followed by the principle of structural risk ...The least squares support vector machine is based on the standard support ...
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Recovery of a Compressed Sensing CT Image Using a Smooth Re-weighted Function- Regularized Least-Squares Algorithm
... It is challenging to recover the required compressed CT (Computed Tomography, CT) image, which is got by transferred through the internet or is stored in a signal library after being compressed. We present a recov- ery ...
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Communication efficient distributed weighted non linear least squares estimation
... distributed algorithm dubbed CREDO − N L (non-linear CREDO ...non-linear least squares problems and generic underlying multi-agent network topologies CREDO − N L ’s strong ...
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Recursive dictionary learning approach exploiting between-channel correlations for EEG signal reconstruction
... based weighted recursive least squares dictionary learning algorithm (CBW-RLS) is proposed that uses between-channel correlations to sparsify EEG ...
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Particle Swarm Optimization and Differentiation Evolution –Based Weighted Least Squares State Estimation
... State estimation is a technique developed to provide an estimate of an unknown system state variable and to quantitatively analyze the estimated state variable before it is used for real time power -now calculations or ...
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The element wise weighted total least squares problem
... The GTLS method is still restrictive for some applications because of the assumption that all rows of D ˜ have equal covariance matrix. A further generalization for the case when the elements of D ˜ are independent, but ...
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An adapted version of the element wise weighted total least squares method for applications in chemometrics
... MLPCA algorithm and the two EW- TLS algorithms to each of the data matrices D of the data set described in Example ...each algorithm and for the different m, n and ...
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Overview of total least squares methods
... alternating least squares algorithm. It applies to the general weighted total least squares problems and is globally convergent, with linear convergence ...The weighted ...
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Teaching Least Squares in Matrix Notation
... The least squares method, a fundamental piece of knowledge for students of all scientific tracks, is often introduced considering the simple linear regression with only two parameters to be ...the ...
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Standard and proportional error model comparison for logistic growth of green algae (Raphidocelis subcapiala)
... A more general methodology motivated by the weighted least squares (as we have presented it in [2]) involves the so-called Generalized Least Squares (GLS) estimator.. Standard residuals [r] ...
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A Partial Least Squares based algorithm for parsimonious variable selection
... Among recent advancements in PLS methodology itself we find that Indahl et. al. [41] propose a new data compression method for estimating optimal latent vari- ables classification and regression problems by combin- ing ...
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Combined state and parameter estimation for Hammerstein systems with time-delay using the Kalman filtering
... gradient algorithm to obtain the parameter estimates, but they did not consider the process noise and the time-delay in the model structure ...based least squares iterative (LSI) algorithm and ...
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Derivative Estimation Based on Difference Sequence via Locally Weighted Least Squares Regression
... A new method is proposed for estimating derivatives of a nonparametric regression function. By applying Taylor expansion technique to a derived symmetric difference sequence, we obtain a sequence of approximate linear ...
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NEW MODEL TRANSFORMATION USING REQUIREMENT TRACEBILITY FROM REQUIREMENT TO UML BEHAVIORAL DESIGN
... Network parallel computing environments to achieve parallel algorithm requires minimizing the communication overhead, and using the medium- grained task parallelism. To this end, according to the inherent ...
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Analysis of partial least squares algorithm based on SBM DEA
... In the table 2 and 4, using the relative average error as reliability criterion and referring to 2 dependent variables, we figure out the relative average errors by utilizing the PLS optimized by SBM algorithm are ...
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