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 each local kernel alignment. Specifically,… More >
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