TY  - EJOU
AU  - Wang, Chuanli  
AU  - Zhu, En  
AU  - Liu, Xinwang  
AU  - Qin, Jiaohua  
AU  - Yin, Jianping  
AU  - Zhao, Kaikai  

TI  - Multiple Kernel Clustering Based on Self-Weighted Local Kernel Alignment
T2  - Computers, Materials \& Continua

PY  - 2019
VL  - 61
IS  - 1
SN  - 1546-2226

AB  - 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, we introduce a new optimization variable- weight-to denote the contribution of each local kernel alignment to clustering performance, and then, weight, kernel combination coefficients and cluster membership are alternately optimized under kernel alignment frame. In addition, we develop a three-step alternate iterative optimization algorithm to address the resultant optimization problem. Broad experiments on five benchmark data sets have been put into effect to evaluate the clustering performance of the proposed algorithm. The experimental results distinctly demonstrate that the proposed algorithm outperforms the typical multiple kernel clustering algorithms, which illustrates the effectiveness of the proposed algorithm.
KW  - Multiple kernel clustering
KW  -  kernel alignment
KW  -  local kernel alignment
KW  -  self-weighted

DO  - 10.32604/cmc.2019.06206