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We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM). ... Comprehensive experiments on 11 benchmark datasets demonstrate that SimpleMKKM outperforms state of the art multi-kernel clustering alternatives. ... • Localized multiple kernel k-means(LMKKM) (Gönen & Margolin, 2014) . ...arXiv:2005.04975v2 fatcat:eze436xyyrecdmmfswf63zemgq
Base partitions can be obtained by performing kernel k-means clustering on all views. ... This type of method is not only computationally efficient, but also more accurate than multiple kernel k-means, and is thus widely used in the multi-view clustering context. ... Multi-view clustering via late fusion alignment maximization (MVC-LFA) Simple multiple kernel k-means (SMKKM)  : SimpleMKKM, or SMKKM, re-formulates the MKKM problem as a minimization-maximization ...doi:10.32604/cmc.2021.013389 fatcat:pdxgd4j4uvfmvbfbfrgft5gl6a
It is then taken as the input of the widely used k-means to generate the cluster labels. ... Comprehensive experiments on multiple benchmark datasets demonstrate the superiority of our algorithm in terms of both clustering accuracy and computational efficiency. ... The algorithm alternatively performs kernel k-means and updates the kernel coefficients. • Localized multiple kernel k-means(LMKKM) (Gönen & Margolin, 2014) . ...dblp:conf/icml/Liu0LWZTT0Z21 fatcat:hnslzj7k6ja5daivx3b5i4q4vy