Abstract
Multi-view spectral clustering has recently received a lot of attention. Existing methods, however, have two problems to be addressed: 1) similarity matrices used in clustering omit the high-order neighbor information, reducing embedding accuracy; 2) two independent procedures of embedding and discretization may result in a suboptimal result, lowering the final performance. To address the abovementioned issues, we propose a unified spectral rotation framework for multi-view clustering using a fused similarity graph. The method begins with establishing similarity graphs for each view and constructing first-order and high-order Laplacian matrices for capturing the hidden similarity among different nodes. Then embedding and discretization procedures are integrated into a new framework for performing a spectral rotation to obtain a global clustering result. Finally, a three-step optimization method for obtaining the final clustering labels is proposed. We conduct extensive experiments on a variety of real-world and synthetic datasets to validate the effectiveness of the proposed algorithm. Our method outperforms state-of-the-art methods by 8.0% on average, according to experimental results. The code of the proposed method is available at https://github.com/lting0120/USRF_FSG.git.
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Acknowledgements
The works described in this paper are supported by The National Natural Science Foundation of China under Grant Nos. 61772210 and U1911201; The Project of Science and Technology in Guangzhou in China under Grant No. 202007040006.
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Liang, Y., Bai, W., Jiang, Y. (2023). A Unified Spectral Rotation Framework Using a Fused Similarity Graph. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14171. Springer, Cham. https://doi.org/10.1007/978-3-031-43418-1_13
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