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 regressi...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Jaén |
| Repositorio: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:ruja.ujaen.es:10953/6338 |
| Acceso en línea: | https://hdl.handle.net/10953/6338 |
| Access Level: | acceso abierto |
| Palabra clave: | Ill-conditioned matrices Linear and nonlinear regression models Partial least squares Quaternion kernel methods N/A |
| Sumario: | 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 |
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