# Multi-view Laplacian Support Vector Machines

arXiv: https://arxiv.org/abs/1307.7024 · HTML: https://mlsanity.com/paper/1307.7024/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2013.

## Built on by (most cited first, 9 of 9)

- [Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning](https://mlsanity.com/paper/2211.09155/) (2022)
- [Enhancing Multiview Synergy: Robust Learning by Exploiting the Wave Loss Function with Consensus and Complementarity Principles](https://mlsanity.com/paper/2408.06819/) (2024)
- [Twin Restricted Kernel Machines for Multiview Classification](https://mlsanity.com/paper/2512.15757/) (2025)
- [Semantic-Preserving Feature Partitioning for Multi-View Ensemble Learning](https://mlsanity.com/paper/2401.06251/) (2024)
- [Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction](https://mlsanity.com/paper/2407.19414/) (2024)
- [Information Theory-Guided Heuristic Progressive Multi-View Coding](https://mlsanity.com/paper/2109.02344/) (2021)
- [Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds](https://mlsanity.com/paper/2411.06276/) (2024)
- [Localisation of Regularised and Multiview Support Vector Machine Learning](https://mlsanity.com/paper/2304.05655/) (2023)
- [Information Theory-Guided Heuristic Progressive Multi-View Coding](https://mlsanity.com/paper/2308.10522/) (2023)

## Abstract (arXiv)

We propose a new approach, multi-view Laplacian support vector machines (SVMs), for semi-supervised learning under the multi-view scenario. It integrates manifold regularization and multi-view regularization into the usual formulation of SVMs and is a natural extension of SVMs from supervised learning to multi-view semi-supervised learning. The function optimization problem in a reproducing kernel Hilbert space is converted to an optimization in a finite-dimensional Euclidean space. After providing a theoretical bound for the generalization performance of the proposed method, we further give a formulation of the empirical Rademacher complexity which affects the bound significantly. From this bound and the empirical Rademacher complexity, we can gain insights into the roles played by different regularization terms to the generalization performance. Experimental results on synthetic and real-world data sets are presented, which validate the effectiveness of the proposed multi-view Laplacian SVMs approach.