# Twin Restricted Kernel Machines for Multiview Classification

arXiv: https://arxiv.org/abs/2512.15757 · HTML: https://mlsanity.com/paper/2512.15757/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [Enhancing Multiview Synergy: Robust Learning by Exploiting the Wave Loss Function with Consensus and Complementarity Principles](https://mlsanity.com/paper/2408.06819/) (2024)
- [Multi-view Laplacian Support Vector Machines](https://mlsanity.com/paper/1307.7024/) (2013)
- [Multiview learning with twin parametric margin SVM](https://mlsanity.com/paper/2408.01981/) (2024)
- [TRKM: Twin Restricted Kernel Machines for Classification and Regression](https://mlsanity.com/paper/2502.15759/) (2025)
- [One Class Restricted Kernel Machines](https://mlsanity.com/paper/2502.10443/) (2025)
- [Multi-view learning with privileged weighted twin support vector machine](https://mlsanity.com/paper/2201.11306/) (2022)

## Built on by (most cited first, 4 of 4)

- [Robust Dual-Model Collaborative Random Vector Functional Link Network](https://mlsanity.com/paper/2608.13628/) (2026)
- [RoBell-RVFL: A Robust Generalized Bell Random Vector Functional Link Network](https://mlsanity.com/paper/2608.16965/) (2026)
- [ECA-BLS: An Efficient Complex-Augmented Broad Learning System](https://mlsanity.com/paper/2608.29763/) (2026)
- [GBFRVFL: Granular-Ball Computing-Based Fuzzy Random Vector Functional Link Network](https://mlsanity.com/paper/2609.29670/) (2026)

## Abstract (arXiv)

Multi-view learning (MVL) is an emerging field in machine learning that focuses on improving generalization performance by leveraging complementary information from multiple perspectives or views. Various multi-view support vector machine (MvSVM) approaches have been developed, demonstrating significant success. Moreover, these models face challenges in effectively capturing decision boundaries in high-dimensional spaces using the kernel trick. They are also prone to errors and struggle with view inconsistencies, which are common in multi-view datasets. In this work, we introduce the multiview twin restricted kernel machine (TMvRKM), a novel model that integrates the strengths of kernel machines with the multiview framework, addressing key computational and generalization challenges associated with traditional kernel-based approaches. Unlike traditional methods that rely on solving large quadratic programming problems (QPPs), the proposed TMvRKM efficiently determines an optimal separating hyperplane through a regularized least squares approach, enhancing both computational efficiency and classification performance. The primal objective of TMvRKM includes a coupling term designed to balance errors across multiple views effectively. By integrating early and late fusion strategies, TMvRKM leverages the collective information from all views during training while remaining flexible to variations specific to individual views. The proposed TMvRKM model is rigorously tested on UCI, KEEL, and AwA benchmark datasets. Both experimental results and statistical analyses consistently highlight its exceptional generalization performance, outperforming baseline models in every scenario.