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  • Open Access


    A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

    Lingyun Xiang1,2, Guohan Zhao1, Qian Li3, Gwang-Jun Kim4,*, Osama Alfarraj5, Amr Tolba5,6

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 267-284, 2021, DOI:10.32604/cmc.2021.013488

    Abstract Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled. However, multiple kernel clustering for incomplete data is a critical yet challenging task. Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task, they may fail when data has a high value-missing rate, and they may easily fall into a local optimum. To address these problems, in this paper, we propose an absent multiple kernel clustering (AMKC) method on incomplete data. The… More >

  • Open Access


    Multiple Kernel Clustering Based on Self-Weighted Local Kernel Alignment

    Chuanli Wang1,2, En Zhu1, Xinwang Liu1, Jiaohua Qin2, Jianping Yin3,*, Kaikai Zhao4

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 409-421, 2019, DOI:10.32604/cmc.2019.06206

    Abstract Multiple kernel clustering based on local kernel alignment has achieved outstanding clustering performance by applying local kernel alignment on each sample. However, we observe that most of existing works usually assume that each local kernel alignment has the equal contribution to clustering performance, while local kernel alignment on different sample actually has different contribution to clustering performance. Therefore this assumption could have a negative effective on clustering performance. To solve this issue, we design a multiple kernel clustering algorithm based on self-weighted local kernel alignment, which can learn a proper weight to clustering performance for… More >

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