# TCGF: A unified tensorized consensus graph framework for multi-view representation learning

arXiv: https://arxiv.org/abs/2309.09987 · HTML: https://mlsanity.com/paper/2309.09987/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 56 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [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)
- [On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization](https://mlsanity.com/paper/1610.07126/) (2016)
- [Attribute-guided Feature Learning Network for Vehicle Re-identification](https://mlsanity.com/paper/2001.03872/) (2020)
- [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)
- [A unified framework based on graph consensus term for multi-view learning](https://mlsanity.com/paper/2105.11781/) (2021)

## Abstract (arXiv)

Multi-view learning techniques have recently gained significant attention in the machine learning domain for their ability to leverage consistency and complementary information across multiple views. However, there remains a lack of sufficient research on generalized multi-view frameworks that unify existing works into a scalable and robust learning framework, as most current works focus on specific styles of multi-view models. Additionally, most multi-view learning works rely heavily on specific-scale scenarios and fail to effectively comprehend multiple scales holistically. These limitations hinder the effective fusion of essential information from multiple views, resulting in poor generalization. To address these limitations, this paper proposes a universal multi-view representation learning framework named Tensorized Consensus Graph Framework (TCGF). Specifically, it first provides a unified framework for existing multi-view works to exploit the representations for individual view, which aims to be suitable for arbitrary assumptions and different-scales datasets. Then, stacks them into a tensor under alignment basics as a high-order representation, allowing for the smooth propagation of consistency and complementary information across all views. Moreover, TCGF proposes learning a consensus embedding shared by adaptively collaborating all views to uncover the essential structure of the multi-view data, which utilizes view-consensus grouping effect to regularize the view-consensus representation. To further facilitate related research, we provide a specific implementation of TCGF for large-scale datasets, which can be efficiently solved by applying the alternating optimization strategy. Experimental results conducted on seven different-scales datasets indicate the superiority of the proposed TCGF against existing state-of-the-art multi-view learning methods.