Abstract
Multiple kernel graph-based clustering (MKGC) has achieved impressive experimental results, primarily due to the superiority of multiple kernel learning (MKL) and the outstanding performance of graph-based clustering. However, many present MKGC methods face the following two disadvantages that pose challenges for further improving clustering performance: (1) these methods always rely on MKL to learn a consensus kernel from multiple base kernels, which may lose some important graph information since graph learning is the key to graph-based clustering, not kernel learning; (2) these methods perform affinity graph learning and subsequent graph-based clustering in two separate steps, which may not be optimal for clustering tasks. To tackle these problems, this paper proposes a new MKGC method for multiple kernel clustering. By directly learning a consensus affinity graph rather than a consensus kernel from multiple base kernels, the important graph information can be preserved. Moreover, by utilizing rank constraint, the cluster indicators are obtained directly without performing the k-means clustering and any graph cut technique. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed method.
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Acknowledgements
This research was supported by the Key Lab of Film and TV Media Technology of Zhejiang Province (Grant no. 2020E10015), and the Zhejiang Province Public Welfare Technology Application Research Project (Grant no. LGF21F020003).
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Wang, Y., Ren, Z. (2022). Multiple Kernel Clustering with Direct Consensus Graph Learning. In: Zhang, JF., Chen, CM., Chu, SC., Kountchev, R. (eds) Advances in Intelligent Systems and Computing. Smart Innovation, Systems and Technologies, vol 268. Springer, Singapore. https://doi.org/10.1007/978-981-16-8048-9_12
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DOI: https://doi.org/10.1007/978-981-16-8048-9_12
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