/UOMvSC

Unified One-step Multi-view Spectral Clustering （IEEE TKDE 2022）

Primary LanguageMATLAB

Unified One-step Multi-view Spectral Clustering (IEEE TKDE 2022)

Authors: Chang Tang, Zhenglai Li (co-first author), Jun Wang, Xinwang Liu, Wei Zhang, En Zhu

This repository contains simple Matlab implementation of our paper UOMvSC.

1. Features

  • Joint exploring the information of graphs and embedding matrices. Under the observation that the inner product of the embedding matrix is a low-rank approximation of the graph, we combine graphs and embedding matrices of different views to obtain a unified graph.

  • Simple but effective one-step clustering manner. We directly capture the discrete clustering indicator matrix from the unified graph with an effective optimization algorithm.

2. Usage

  • Prepare the data:

    • Partial datasets used in our paper can be downloaded from BaiduYun(s3u3).

  • Prerequisites for Matlab:

    • Test on Matlab R2018a

  • Conduct clustering

3. Citation

Please cite our paper if you find the work useful:

@article{Li_2022_UOMvSC,
     author={Tang, Chang and Li, Zhenglai and Wang, Jun and Liu, Xinwang and Zhang, Wei and Zhu, En},
    journal={IEEE Transactions on Knowledge and Data Engineering}, 
    title={Unified One-step Multi-view Spectral Clustering}, 
    year={2022},
    volume={},
    number={},
    pages={1-1},
    doi={10.1109/TKDE.2022.3172687}
    }