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[TCSS-2023] Official code for "Robust Weighted Low-Rank Tensor Approximation for Multiview Clustering With Mixed Noise" (improved)

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Robust Weighted Low-Rank Tensor Approximation for Multiview Clustering With Mixed Noise


Readme for RWLTA

version Dec. 22, 2023


About the robustness experiments

The algorithms for feature extraction and noise addition used in the section of the experiments can be found in the repository image_feature_intensity_LBP_Gabor

Brief introduction

This repository includes the MATLAB code of the paper X. Pu, H. Che, B. Pan, M. -F. Leung and S. Wen, "Robust Weighted Low-Rank Tensor Approximation for Multiview Clustering With Mixed Noise," in IEEE Transactions on Computational Social Systems, doi: 10.1109/TCSS.2023.3331366

You will find an example of using this code in the repository (Demo_bbcsport.d, Demo_bbc4.m, Demo_msrc.m) and the corresponding datasets (bbcsport.mat, BBC.mat, msrc.mat)

Recommended operating environment

MATLAB R2022a, Windows 10, 2.90-4.20 GHz AMD R7-4800H CPU, and 32 GB main memory.

Parameter tuning tips:

  • For
    Unable to render expression.

    $w$
    , we suggest it based on a priori knowledge, specifically, the range of
    Unable to render expression.

    $w_i$
    can be set as
    Unable to render expression.

    $[0.1, 10]$
    . We suggest select
    Unable to render expression.

    $\lambda_1$
    ,
    Unable to render expression.

    $\lambda_2$
    , and
    Unable to render expression.

    $\lambda_3$
    from interval
    Unable to render expression.

    $[0.05, 10]$
    . For
    Unable to render expression.

    $\theta$
    , one can select from {0.2, 0.3, 0.5, 1, 3, 5}.
  • For other parameters, you can keep the default values.

Citation

If you find our repo helpful, please consider leaving a star or citing our paper :)

@ARTICLE{RWLTA,
  author={Pu, Xinyu and Che, Hangjun and Pan, Baicheng and Leung, Man-Fai and Wen, Shiping},
  journal={IEEE Transactions on Computational Social Systems}, 
  title={Robust Weighted Low-Rank Tensor Approximation for Multiview Clustering With Mixed Noise}, 
  year={2023},
  volume={},
  number={},
  pages={1-18},
  doi={10.1109/TCSS.2023.3331366}
}

ATTENTION

This package is free for academic usage.

For other purposes, please contact Hangjun Che (hjche123@swu.edu.cn)

This package was developed by Xinyu Pu.

For any problem concerning the code, please feel free to contact Xinyu Pu (pushyu404@163.com)

Contact

Hangjun Che, SWU

Xinyu Pu, SWU


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[TCSS-2023] Official code for "Robust Weighted Low-Rank Tensor Approximation for Multiview Clustering With Mixed Noise" (improved)

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