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Sparse Reconstructive Evidential Clustering for Multi-View Data
Chaoyu Gong; Yang You
Source PublicationIEEE/CAA Journal of Automatica Sinica
AbstractAlthough 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.
KeywordEvidence theory multi-view clustering (MVC) optimization sparse reconstruction
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Document Type期刊论文
Collection学术期刊_IEEE/CAA Journal of Automatica Sinica
Recommended Citation
GB/T 7714
Chaoyu Gong,Yang You. Sparse Reconstructive Evidential Clustering for Multi-View Data[J]. IEEE/CAA Journal of Automatica Sinica,2024,11(2):459-473.
APA Chaoyu Gong,&Yang You.(2024).Sparse Reconstructive Evidential Clustering for Multi-View Data.IEEE/CAA Journal of Automatica Sinica,11(2),459-473.
MLA Chaoyu Gong,et al."Sparse Reconstructive Evidential Clustering for Multi-View Data".IEEE/CAA Journal of Automatica Sinica 11.2(2024):459-473.
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