Abstract
Compared with Single-view clustering, Multi-view clustering analysis exploits more hidden information. Multiple kernel learning (MKL) performs its superiority in heterogeneous sources and solves the problem of selection of kernel functions. Many existing multi-view literatures based on MKL consider instances in each view equally and overlook the difference among them. In this paper, a multi-view clustering algorithm based on variable weight and MKL (called MVMKC) is proposed. MVMKC improves clustering quality with more-refined analyses on data. To be specific, it uses an improved weighted Gaussian kernel rather than the traditional combined kernel function. Meanwhile, variable weights are introduced to measure the contribution of instance in different views. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach.
Y. Yang—This work is supported by the Natioanl Science Foundation of China (Nos. 61572407 and 61603313), the Project of National Science and Technology Support Program (No. 2015BAH19F02).
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Zhang, P., Yang, Y., Peng, B., He, M. (2017). Multi-view Clustering Algorithm Based on Variable Weight and MKL. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_48
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