Lingyun Xiang^1,2, Guohan Zhao¹, Qian Li³, Gwang-Jun Kim^4,*, Osama Alfarraj⁵, Amr Tolba^5,6

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 >

Chuanli Wang^1,2, En Zhu¹, Xinwang Liu¹, Jiaohua Qin², Jianping Yin^3,*, 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 each local kernel alignment. Specifically,… More >