Abstract
Multiple kernel algorithms in a late fusion manner have been widely used because of its excellent performance and high efficiency in multi-view clustering (MVC). The existing MVC algorithms via late fusion obtain a consensus clustering indicator matrix through the linear combination of the base clustering indicator matrix. As a result, the optimal consensus indicator matrix’s searching space reduces, and the clustering effect is limited. To learn more information from the base clustering indicator matrices, we construct a consensus similarity matrix as the input of the spectral clustering algorithm. Furthermore, we design an effective iterative algorithm to solve the new resultant optimization problem. Extensive experiments on 11 multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed algorithm.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (project No. 61319020).
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Liu, D., Liang, W., Zhang, H., Zhao, W., Hu, K. (2021). Late Fusion Multi-view Clustering with Learned Consensus Similarity Matrix. In: He, K., Zhong, C., Cai, Z., Yin, Y. (eds) Theoretical Computer Science. NCTCS 2020. Communications in Computer and Information Science, vol 1352. Springer, Singapore. https://doi.org/10.1007/978-981-16-1877-2_6
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DOI: https://doi.org/10.1007/978-981-16-1877-2_6
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