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Volume 11 Issue 2
Feb.  2024

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(2): 459-473
C. Gong and  Y. You,  “Sparse reconstructive evidential clustering for multi-view data,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 459–473, Feb. 2024. doi: 10.1109/JAS.2023.123579
Citation: C. Gong and  Y. You,  “Sparse reconstructive evidential clustering for multi-view data,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 459–473, Feb. 2024. doi: 10.1109/JAS.2023.123579
shu

Sparse Reconstructive Evidential Clustering for Multi-View Data

doi: 10.1109/JAS.2023.123579
Funds:  This work was supported in part by NUS startup grant and the National Natural Science Foundation of China (52076037)
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    Abstract

  • Although many multi-view clustering (MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects, which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm (SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional human-readable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides, SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.

     

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    Highlights

    • Estimate the cluster number for a multi-view clustering problem
    • Identify the cluster center in each cluster of multi-view data
    • Use a more fine-grained partition to cluster multi-view data

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