# Multi-View Clustering from the Perspective of Mutual Information

arXiv: https://arxiv.org/abs/2302.08743 · HTML: https://mlsanity.com/paper/2302.08743/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Learning Robust Representations via Multi-View Information Bottleneck](https://mlsanity.com/paper/2002.07017/) (2020)
- [Multi-level Feature Learning for Contrastive Multi-view Clustering](https://mlsanity.com/paper/2106.11193/) (2021)
- [On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization](https://mlsanity.com/paper/1610.07126/) (2016)
- [Multi-view Deep Subspace Clustering Networks](https://mlsanity.com/paper/1908.01978/) (2019)
- [Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization](https://mlsanity.com/paper/2012.01405/) (2020)
- [Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information](https://mlsanity.com/paper/2004.03991/) (2020)
- [Active Feature Selection for the Mutual Information Criterion](https://mlsanity.com/paper/2012.06979/) (2020)

## Built on by (most cited first, 1 of 1)

- [Efficient and Effective Deep Multi-view Subspace Clustering](https://mlsanity.com/paper/2310.09718/) (2023)

## Abstract (arXiv)

Exploring the complementary information of multi-view data to improve clustering effects is a crucial issue in multi-view clustering. In this paper, we propose a novel model based on information theory termed Informative Multi-View Clustering (IMVC), which extracts the common and view-specific information hidden in multi-view data and constructs a clustering-oriented comprehensive representation. More specifically, we concatenate multiple features into a unified feature representation, then pass it through a encoder to retrieve the common representation across views. Simultaneously, the features of each view are sent to a encoder to produce a compact view-specific representation, respectively. Thus, we constrain the mutual information between the common representation and view-specific representations to be minimal for obtaining multi-level information. Further, the common representation and view-specific representation are spliced to model the refined representation of each view, which is fed into a decoder to reconstruct the initial data with maximizing their mutual information. In order to form a comprehensive representation, the common representation and all view-specific representations are concatenated. Furthermore, to accommodate the comprehensive representation better for the clustering task, we maximize the mutual information between an instance and its k-nearest neighbors to enhance the intra-cluster aggregation, thus inducing well separation of different clusters at the overall aspect. Finally, we conduct extensive experiments on six benchmark datasets, and the experimental results indicate that the proposed IMVC outperforms other methods.