Orthogonal Least Squares with ERR detection
Orthogonal least squares regression with tunable kernels
... A novel technique is proposed to construct sparse regression models based on the orthogonal least squares method with tunable kernels. The proposed technique tunes the centre vector and diagonal ...
5
Joint k-step analysis of Orthogonal Matching Pursuit and Orthogonal Least Squares
... of Orthogonal Matching Pur- suit (OMP) using the Exact Recovery Condition (ERC) [1] is extended to a first exact recovery analysis of Orthogonal Least Squares ...
21
Least-Squares Adaptive Control Using Chebyshev Orthogonal Polynomials
... Chebyshev orthogonal polynomials as basis functions in a least-squares functional ...of orthogonal basis functions improves the function approximation significantly and enables better ...
21
Orthogonal basis functions in discrete least-squares rational approximation
... discrete least-squares rational approximation can be placed in a unifying ...appropriate orthogonal basis, much like in the classical Forsythe algorithm [ 15 ] for discrete ...
20
Regularized orthogonal least squares algorithm for constructing radial basis function networks
... The orthogonal least squares (OLS) algorithm (Chen et al. 1991) is a n efficient procedure for learning a parsimonious radial basis function (RBF) network.. A simple mechanism can be[r] ...
10
A multiple sequential orthogonal least squares algorithm for feature ranking and subset selection
... Principal component analysis is a useful tool for dimensionality reduction. Principal components, however, suffer from two main deficiencies: Principal components always involve all the original variables and are usually ...
18
Tree Based Orthogonal Least Squares Regression with Repeated Weighted Boosting Search
... V. C ONCLUSIONS In order to a sparse representation, this paper proposes a novel tree based orthogonal least squares regression. Unlike most of the conventional OLSR, the new method keeps the k ...
9
Finite impulse response filter design using a forward orthogonal least squares algorithm
... standard least squares problem. Compared with simple least squares algorithms, the orthogonal least squares method has been demonstrated to be a powerful means to achieve ...
7
Orthogonal Latin Squares Encoders and Syndrome Computation with Concurrent Error Detection
... Eccs, Orthogonal Latin Squares (OLS) Codes Have Gained Renewed Interest For Memory Protection Due Tot heir Modularity And The Simplicity Of The Decoding Algorithm That Enables Low Delay ...Error ...
5
Development of adaptive p-step RBF network model with recursive orthogonal least squares training
... Abstract. An adaptive 𝑝 -step prediction model for nonlinear dynamic processes is developed in this paper, and implemented with a radial basis function (RBF) network. The model can predict output for multi-step ahead ...
25
Local Regularization Assisted Orthogonal Least Squares Regression
... transformed orthogonal space, but the selected subset of the orthogonal regressors or bases corresponds precisely to a subset of the original model bases, that is, the algorithm actually selects a subset of ...
27
Orthogonal least squares regression: An efficient approach for parsimonious modelling from large data
... Harris, “Particle swarm optimization aided orthogonal forward regression for unified data modelling,” IEEE Trans.. Imbalanced Classification[r] ...
54
A modified orthogonal forward regression least-squares algorithm for system modelling from noisy regressors
... The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify th[r] ...
21
The Method of Least Squares
... of least squares OLS can be interpreted in a geometrical framework as an orthog- onal projection of the data vector onto the space defined by the independent ...is orthogonal because the predicted ...
7
On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... It is evident that the linear system (20) is redundant: the last h rows of the complete matrix [K|i] can be deleted thus giving rise to a system of h + 1 equations in 2h + 1 unknown variables, let us denote it by K † σ = ...
23
On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... . It is evident that the linear system (20) is redundant: the last h rows of the complete matrix [ K |i] can be deleted thus giving rise to a system of h + 1 equations in 2h + 1 unknown variables, let us denote it by K † ...
23
Least Squares Percentage Regression
... TYPE I ERROR RATES IN BEST SUBSET REGRESSION 406 Conclusion A computer based heuristic that uses randomization has been described. The algorithm allows control of Type I error rate for the overall statistical ...
277
4.3 Least Squares Approximations
... Main point: Now A T A is diagonal. We can solve separately for C D 7 3 and D D 6 8 . The zeros in A T A are dot products of perpendicular columns in A. The diagonal matrix A T A, with entries m D 3 and t 1 2 C t 2 2 C t ...
12
Multitrait Least Squares for Quantitative Trait Loci Detection
... tween the traits generated by the QTL is small and, determinants of sums of squares matrices). The prob- lem is one of testing, as the null hypothesis will fre- hence, ignoring this should not bias the results. In ...
13
Compressed Least-Squares Regression
... Related works: Using dimension reduction and random projections in various learning areas has received considerable interest over the past few years. In [7], the authors use a SVM algorithm in a compressed space for the ...
11