# Locality Relationship Constrained Multi-view Clustering Framework

arXiv: https://arxiv.org/abs/2107.05073 · HTML: https://mlsanity.com/paper/2107.05073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 11 Jul 2021.

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

- [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)
- [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)
- [Multi-View Intact Space Learning](https://mlsanity.com/paper/1904.02340/) (2019)
- [Discriminative Feature and Dictionary Learning with Part-aware Model for Vehicle Re-identification](https://mlsanity.com/paper/2003.07139/) (2020)
- [Co-regularized Multi-view Sparse Reconstruction Embedding for Dimension Reduction](https://mlsanity.com/paper/1904.08499/) (2019)

## Abstract (arXiv)

In most practical applications, it's common to utilize multiple features from different views to represent one object. Among these works, multi-view subspace-based clustering has gained extensive attention from many researchers, which aims to provide clustering solutions to multi-view data. However, most existing methods fail to take full use of the locality geometric structure and similarity relationship among samples under the multi-view scenario. To solve these issues, we propose a novel multi-view learning method with locality relationship constraint to explore the problem of multi-view clustering, called Locality Relationship Constrained Multi-view Clustering Framework (LRC-MCF). LRC-MCF aims to explore the diversity, geometric, consensus and complementary information among different views, by capturing the locality relationship information and the common similarity relationships among multiple views. Moreover, LRC-MCF takes sufficient consideration to weights of different views in finding the common-view locality structure and straightforwardly produce the final clusters. To effectually reduce the redundancy of the learned representations, the low-rank constraint on the common similarity matrix is considered additionally. To solve the minimization problem of LRC-MCF, an Alternating Direction Minimization (ADM) method is provided to iteratively calculate all variables LRC-MCF. Extensive experimental results on seven benchmark multi-view datasets validate the effectiveness of the LRC-MCF method.