XGRVFL-MV: Residual-Coupled Graph-Embedded Multi-View Random Vector Functional Link Network with FleXi Guardian Loss

2026-07-25

AI Summary

This work addresses key challenges in multi-view classification—preserving geometric structure, modeling inter-view relationships, and ensuring robustness to large residuals—by proposing a novel multi-view Random Vector Functional Link (RVFL) network that integrates graph embedding with a residual coupling mechanism. The approach constructs intrinsic and penalty graphs via locality-preserving Fisher discriminant analysis to retain the geometric structure of each view, introduces a residual coupling term to enforce prediction consistency across views, and incorporates, for the first time in multi-view RVFL networks, a bounded asymmetric FleXi Guardian loss to enhance robustness, optimized via Nesterov-accelerated gradient descent. Extensive experiments on UCI, KEEL, AwA, and Corel5k datasets demonstrate statistically significant superiority over state-of-the-art methods, with sensitivity analyses further confirming the model’s effectiveness and stability.

Abstract

Random Vector Functional Link (RVFL) networks provide an efficient randomized learning framework for classification. Existing multi-view RVFL methods utilize complementary information from multiple views. However, preserving view-specific geometric structure, limiting the influence of large prediction residuals, and modeling relationships between multiple views remain challenging. This paper proposes a Residual-Coupled Graph-Embedded Multi-View RVFL model with fleXi guardian loss (XGRVFL-MV) for multi-view classification. The proposed model constructs RVFL representation for each view, incorporates graph embedding with intrinsic and penalty graphs constructed using the Local Fisher Discriminant Analysis weighting scheme. It also uses the bounded and asymmetric FleXi Guardian (XG) loss for residual learning. A residual-coupling term is introduced to encourage consistency among view-specific prediction residuals while preserving view-specific representations. The resulting optimization problem is solved using an inversion-free first-order optimization procedure based on Nesterov accelerated gradient descent. We evaluate the proposed model on UCI, KEEL, AwA, and Corel5k benchmark datasets. Experimental results, together with statistical analyses and hyperparameter sensitivity analyses, show that XGRVFL-MV achieves competitive classification performance compared with the baseline methods across the evaluated benchmark datasets.

Problem

• multi-view classification
• geometric structure preservation
• prediction residuals
• view relationships
• randomized learning

Innovation

• Multi-view learning
• Random Vector Functional Link network
• Graph embedding
• Residual coupling
• FleXi Guardian loss