Multi-view Clustering and Evaluations Explained

Multi-view Clustering and Evaluations Explained
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Discover the significance of multi-view clustering, its advantages over single-view clustering, and explore various frameworks and algorithms used in multi-view clustering for tasks like detecting deceptive reviews and spectral clustering. Dive deeper into Linked Matrix Factorization and Multi-view Spectral Clustering for a comprehensive understanding of this cutting-edge concept.

  • Multi-view Clustering
  • Evaluations
  • Frameworks
  • Algorithms
  • Spectral Clustering
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Uploaded on Apr 29, 2025 | 3 Views

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Presentation Transcript

  1. Multi-view clustering and evaluations 2019/11/15

  2. Why I need multi-view clustering? Goal Challenge Single-view clustering? Theoretical support

  3. A Multiview Clustering Framework for Detecting Deceptive Reviews

  4. Multi-view Clustering (MvC): A Survey Co-training style algorithm Multi-kernel learning Multi-view graph clustering Graph based MvC Network based MvC Spectral based MvC Multi-view subspace clustering Multi-task multi-view clustering

  5. 1. Linked Matrix Factorization(LMF) to get a fusion graph LMF: to approximate a graph ? ? ??. In multiple graphs, minimizing Experiments to be done. Tang, Wei, Zhengdong Lu, and Inderjit S. Dhillon. "Clustering with multiple graphs." 2009 Ninth IEEE International Conference on Data Mining. IEEE, 2009.

  6. 2. Multi-view spectral clustering Theoretical explanation Zhenyu Huang, et al. Multi-view Spectral Clustering Network IJCAI. 2019.

  7. 2. Multi-view spectral clustering

  8. 2. Multi-view spectral clustering

  9. Thanks

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