  Multiview learning (MVL) seeks to leverage the benefits of diverse
perspectives to complement each other, effectively extracting and utilizing the
latent information within the dataset. Several twin support vector
machine-based MVL (MvTSVM) models have been introduced and demonstrated
outstanding performance in various learning tasks. However, MvTSVM-based models
face significant challenges in the form of computational complexity due to four
matrix inversions, the need to reformulate optimization problems in order to
employ kernel-generated surfaces for handling non-linear cases, and the
constraint of uniform noise assumption in the training data. Particularly in
cases where the data possesses a heteroscedastic error structure, these
challenges become even more pronounced. In view of the aforementioned
challenges, we propose multiview twin parametric margin support vector machine
(MvTPMSVM). MvTPMSVM constructs parametric hyperplanes with the goal of
maximizing the parametric margin between two classes, aiming to regulate and
manage the impact of the heteroscedastic noise structure existing within the
data. The proposed MvTPMSVM model avoids the explicit computation of matrix
inversions in the dual formulation, leading to enhanced computational
efficiency. We perform an extensive assessment of the MvTPMSVM model using
benchmark datasets such as UCI, KEEL, synthetic, and Animals with Attributes
(AwA). Our experimental results, coupled with rigorous statistical analyses,
confirm the superior generalization capabilities of the proposed MvTPMSVM model
compared to the baseline models. The source code of the proposed MvTPMSVM model
is available at \url{https://github.com/mtanveer1/MvTPMSVM}.
