Ruiz Molina, Juan CarlosNavarro Moreno, JesúsFernández Alcalá, Rosa MªJiménez López, José D.2025-01-192025-01-19202410.1016/J.JFRANKLIN.2024.106785https://hdl.handle.net/10953/4151The ill-conditioned matrix problem in quaternion linear regression models is addressed in this paper and several dimension-reduction based regression methods for circumventing this problem are suggested. The algorithms are formulated in a general way and can be easily adapted to different scenarios: widely linear, semi-widely linear and strictly linear processing, in accordance with the properness properties presented by quaternion random vectors. A comparison with existing solutions is carried out by using both laboratory data and a color face database.engCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/Ill-conditioned MatricesPartial Least SquaresQuaternion Regression ModelsProperness Propertiesinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccess