# A unified framework based on graph consensus term for multi-view learning

arXiv: https://arxiv.org/abs/2105.11781 · HTML: https://mlsanity.com/paper/2105.11781/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 90 references that are in the map.
- First posted on arXiv 25 May 2021.

## Builds on (most cited first, 16 of 16)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Deep Graph Contrastive Representation Learning](https://mlsanity.com/paper/2006.04131/) (2020)
- [Graph Contrastive Learning with Adaptive Augmentation](https://mlsanity.com/paper/2010.14945/) (2020)
- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [A Survey on Multi-View Clustering](https://mlsanity.com/paper/1712.06246/) (2017)
- [Multi-graph Fusion for Multi-view Spectral Clustering](https://mlsanity.com/paper/1909.06940/) (2019)
- [Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition](https://mlsanity.com/paper/1910.11012/) (2019)
- [Kernelized Multiview Subspace Analysis by Self-weighted Learning](https://mlsanity.com/paper/1911.10357/) (2019)
- [CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning](https://mlsanity.com/paper/2009.01674/) (2020)
- [Attribute-guided Feature Learning Network for Vehicle Re-identification](https://mlsanity.com/paper/2001.03872/) (2020)
- [Generalized Multi-view Embedding for Visual Recognition and Cross-modal Retrieval](https://mlsanity.com/paper/1605.09696/) (2016)
- [Multiview Hessian Discriminative Sparse Coding for Image Annotation](https://mlsanity.com/paper/1307.3811/) (2013)
- [Multi-view Locality Low-rank Embedding for Dimension Reduction](https://mlsanity.com/paper/1905.08138/) (2019)
- [Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation](https://mlsanity.com/paper/2006.10520/) (2020)
- [The Similarity-Consensus Regularized Multi-view Learning for Dimension Reduction](https://mlsanity.com/paper/1911.07656/) (2019)

## Built on by (most cited first, 2 of 2)

- [TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis](https://mlsanity.com/paper/2309.07947/) (2023)
- [TCGF: A unified tensorized consensus graph framework for multi-view representation learning](https://mlsanity.com/paper/2309.09987/) (2023)

## Abstract (arXiv)

In recent years, multi-view learning technologies for various applications have attracted a surge of interest. Due to more compatible and complementary information from multiple views, existing multi-view methods could achieve more promising performance than conventional single-view methods in most situations. However, there are still no sufficient researches on the unified framework in existing multi-view works. Meanwhile, how to efficiently integrate multi-view information is still full of challenges. In this paper, we propose a novel multi-view learning framework, which aims to leverage most existing graph embedding works into a unified formula via introducing the graph consensus term. In particular, our method explores the graph structure in each view independently to preserve the diversity property of graph embedding methods. Meanwhile, we choose heterogeneous graphs to construct the graph consensus term to explore the correlations among multiple views jointly. To this end, the diversity and complementary information among different views could be simultaneously considered. Furthermore, the proposed framework is utilized to implement the multi-view extension of Locality Linear Embedding, named Multi-view Locality Linear Embedding (MvLLE), which could be efficiently solved by applying the alternating optimization strategy. Empirical validations conducted on six benchmark datasets can show the effectiveness of our proposed method.