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Robust multi-view clustering in latent low-rank space with discrepancy induction

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Abstract

Due to the fantastic ability to capture consistent and complementary information between views, multi-view graph clustering has attracted extensive research attention. However, multi-view data are mostly high-dimensional, which may contain many redundant and irrelevant features. At the same time, the original data are usually contaminated by noise and outliers, which may destroy the intrinsic structural information of data and reduce the reliability of the affinity matrix learned. Moreover, most models assign different weights to each view to fully consider the relation between views. The intrinsic information of some views cannot be fully utilized due to the small view weights assigned to them. To deal with these problems, in this study, we propose a robust multi-view clustering model by combining low-dimensional and low-rank latent space learning, self-representation learning, and multi-view discrepancy induction fusion into a unified framework. Specifically, the original high-dimensional data is first reconstructed in a low-dimensional and low-rank space. A self-representation learning method is used to learn the reliable affinity matrix for each view. Furthermore, the Hilbert-Schmidt independence criterion is used as a discrepancy induction module for the complementary fusion of views. Finally, to preserve the data’s local geometric structure, a simple adaptive graph regularization term is applied to the affinity matrix for each view. The comprehensive experiments on six benchmark datasets validate that the proposed model outperforms the six state-of-the-art comparison models in robustness and clustering performance.

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Data Availability

The datasets supporting the conclusions of this article are included within the corresponding references.

Notes

  1. The pictures are respectively downloaded from links: https://p0.ssl.cdn.btime.com/t01ea210a7841354c87.jpg?size=421x306, https://i0.hdslb.com/bfs/bangumi/image/43d66dbd27f8cd68d59c7d583226666651e396e3.png@279w_372h.webp, https://pic1.zhimg.com/v2-68f94466225ad77993047fb1ec0605c0_r.jpg

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61976182 and 62076171), Sichuan Key R &D project (2020YFG0035), the Natural Science Foundation of Sichuan Province (2022NS552FSC0898), and the Sichuan Science and Technology Achievements Transfer and Transformation Dem-onstration Project (2022ZHCG0005).

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Author notes

  1. Hongmei Chen, Tianrui Li and Xiaoling Yang contributed equally to this work.

Authors and Affiliations

  1. School of Computing and Artificial Intelligence, Southwest Jiaotong University, 611756, Chengdu, China

    Bo Xiong, Hongmei Chen, Tianrui Li & Xiaoling Yang

  2. National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, 611756, Chengdu, China

    Bo Xiong, Hongmei Chen, Tianrui Li & Xiaoling Yang

Authors
  1. Bo Xiong
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  2. Hongmei Chen
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  3. Tianrui Li
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  4. Xiaoling Yang
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Contributions

Bo Xiong: Conceptualization, Metho-dology, Software, Writing - Original Draft, Validation, Formal analysis, Investigation, Visualization, Writing - Review & Editing. Hongmei Chen: Writing - Review & Editing, Supervision, Visualization, Investigation, Formal analysis, Validation. Tianrui Li: Writing - Review & Editing, Supervision, Resources. Xiaoling Yang: Writing - Review & Editing.

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Correspondence to Hongmei Chen.

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Xiong, B., Chen, H., Li, T. et al. Robust multi-view clustering in latent low-rank space with discrepancy induction. Appl Intell 53, 23655–23674 (2023). https://doi.org/10.1007/s10489-023-04699-x

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