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 AMKC method first clusters the… More >
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