<p align=center>Consensus Graph Learning for Multi-view Clustering (IEEE TMM 2021)</p>

Authors: Zhenglai Li, Chang Tang, Xinwang Liu, Xiao Zheng, Guanghui Yue, Wei Zhang, En Zhu

This repository contains simple Matlab and Python implementations of our paper CGL.

1. Overview

<p align="center"> <img src="assest/CGL.jpg"/> <br /> </p>

Framework of the proposed CGL method. Multi-view similarity graphs ${\mathbf{W}^{(v)}}{v=1}^V$ are generated from multi-view data ${\mathbf{X}^{(v)}}{v=1}^V$ in advance. Multi-view embedded representations \textcolor[rgb]{0,0,1}{${\mathbf{H}^{(v)}}{v=1}^V$} are obtained via (a) spectral embedding. To effectively capture the global consistency among multiple views, a low rank tensor $\mathcal{T}$ is learned from a corrupted tensor $\mathcal{B}$, which is constructed by stacking the inner product of normalized embedded representations ${ \bar{\mathbf{H}}^{(v)}\bar{\mathbf{H}}^{(v)\top}}{v = 1}^V$ into a third-order tensor form. We further integrate the (a) spectral embedding and (b) low rank tensor representation learning into a unified optimization framework to achieve mutual promotion. Finally, the consensus graph $\mathbf{S}$ can be learned in the embedded space. <br>

2. Usage

• Prepare the data:

  • The ORL dataset can be downloaded from Google_Drive.
  • The other datasts can be downloaded from BaiduYun(s3u3).

• Prerequisites for Python:

  • Creating a virtual environment in terminal: conda create -n CGL python=3.9
  • Installing necessary packages: pip install -r requirements.txt

• Prerequisites for Matlab:

  • Test on Matlab R2018a

• Conduct clustering

3. Citation

Please cite our paper if you find the work useful:

@article{Li_2021_CGL,
    author={Li, Zhenglai and Tang, Chang and Liu, Xinwang and Zheng, Xiao and Zhang, Wei and Zhu, En},
    journal={IEEE Transactions on Multimedia}, 
    title={Consensus Graph Learning for Multi-View Clustering}, 
    year={2022},
    volume={24},
    number={},
    pages={2461-2472},
    doi={10.1109/TMM.2021.3081930}
    }