The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Large-Scale Multi-View Spectral Clustering via Bipartite Graph
Open accessAbstract
In this paper, we address the problem of large-scale multi-view spectral clustering. In many real-world applications, data can be represented in various heterogeneous features or views. Different views often provide different aspects of information that are complementary to each other. Several previous methods of clustering have demonstrated that better accuracy can be achieved using integrated information of all the views than just using each view individually. One important class of such methods is multi-view spectral clustering, which is based on graph Laplacian. However, existing methods are not applicable to large-scale problem for their high computational complexity. To this end, we propose a novel large-scale multi-view spectral clustering approach based on the bipartite graph. Our method uses local manifold fusion to integrate heterogeneous features. To improve efficiency, we approximate the similarity graphs using bipartite graphs. Furthermore, we show that our method can be easily extended to handle the out-of-sample problem. Extensive experimental results on five benchmark datasets demonstrate the effectiveness and efficiency of the proposed method, where our method runs up to nearly 3000 times faster than the state-of-the-art methods.
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Cites
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Multi-view learning overview: Recent progress and new challenges
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Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
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Self-citationGraph Learning for Multiview Clustering
Zhan, K., Zhang, C., Guan, J., & Wang, J. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2751646 · Cited by 543
Fast Parameter-Free Multi-View Subspace Clustering With Consensus Anchor Guidance
Wang, S., Liu, X., Zhu, X., Zhang, P., Zhang, Y., Gao, F., & Zhu, E. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3131941 · Cited by 517
Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method
Li, X., Zhang, H., Wang, R., & Nie, F. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3011148 · Cited by 499
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Metadata
- Authors
- Li, Y., Nie, F., Huang, H., & Huang, J.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2015-02-21
- Volume
- 29
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W297461772
- Cited by
- 585
- References
- 30
- FWCI
- 16.29
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Li, Y., Nie, F., Huang, H., & Huang, J. (2015). Large-Scale Multi-View Spectral Clustering via Bipartite Graph. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v29i1.9598
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