Multiview Ensemble Deep Generalized Eigenvalue Random Vector Functional Link

The Random Vector Functional Link (RVFL) framework provides a simple and effective classification solution using a single-layer feedforward structure with randomization. The ensemble and deep variants of RVFL utilize multiple layers to improve performance, whereas the generalized eigenvalue-based deep RVFL (edGERVFL) solves a generalized eigenvalue problem integrated with the deep RVFL architecture. Multiview learning methods use multiple data views for enhanced generalization, but existing deep RVFL-based models do not incorporate multiview information. We formulate a Multiview Ensemble Deep Generalized Eigenvalue RVFL (MV-edGERVFL) and its variants — Multiview Kernel edGERVFL, Multiview Improved edGERVFL, and Multiview Kernel Improved edGERVFL — to achieve improved classification performance with multiple views of complex and non-linear data. The proposed model integrates multiview feature extraction with a deep ensemble of randomized networks based on generalized eigenvalue-based classification. To enhance applicability, we introduce three specialized variants that address non-linearity and singularity while retaining a closed-form solution. Multiview Kernel edGERVFL performs kernel mapping for non-linear separability. The Multiview Improved edGERVFL reduces singularity issues in correlated or sparse data. The Multiview Kernel Improved edGERVFL handles non-linearity and singularity together for improved stability. Experiments on UCI and AWA datasets offer consistent improvements when multiple data views are available.