Quaternion Kernel Partial Least Squares Regression Algorithms
Copyright policy )Quaternion Kernel Partial Least Squares Regression Algorithms
This work provides three quaternion kernel partial least squares (PLS) algorithms for linear and nonlinear regressions. Firstly, the problem of large ill-conditioned matrices is tackled and two specifically designed linear kernel algorithms are suggested. Secondly, since PLS can present low regression accuracy and prediction performance for nonlinear data, a kernel algorithm for performing quaternion nonlinear regression is also given. Computational results and discussion illustrate the relative merits of the algorithms proposed over closely related regression methods
Ill-conditioned matrices, Linear regression; mixed models, ill-conditioned matrices, N/A, Partial least squares, Least squares and related methods for stochastic control systems, quaternion kernel methods, partial least squares, Linear and nonlinear regression models, linear and nonlinear regression models, Quaternion kernel methods
Ill-conditioned matrices, Linear regression; mixed models, ill-conditioned matrices, N/A, Partial least squares, Least squares and related methods for stochastic control systems, quaternion kernel methods, partial least squares, Linear and nonlinear regression models, linear and nonlinear regression models, Quaternion kernel methods
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