Fuzzy Clustering with Multiple Kernels in Feature Space

被引:0
|
作者
Baili, Naouel [1 ]
Frigui, Hichem [1 ]
机构
[1] Univ Louisville, CECS Dept, Multimedia Res Lab, Louisville, KY 40292 USA
来源
2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2012年
关键词
Fuzzy clustering; multiple kernels; feature space; kernel weights;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
While classical kernel-based clustering algorithms are based on a single kernel, in practice it is often desirable to base clustering on combination of multiple kernels. In [1], we considered a fuzzy c-means with multiple kernels in observation space (FCMK-OS) algorithm which constructs the kernel from a number of Gaussian kernels and learns a resolution specific weight for each kernel function in each cluster. The FCMK-OS did not have a closed form expression to update the kernel weights. Moreover, it derives the fuzzy c-means in input space with kernelization of the metric. Thus, it can not handle nonlinear partitioning of the data. In this paper, we propose a fuzzy c-means with multiple kernels in feature space (FCMK-FS) algorithm which extends the fuzzy c-means algorithm with an adaptive multiple kernel learning setting. The incorporation of multiple kernels and unsupervised adjusting of the kernel weights in each cluster makes the choice of the kernels less crucial and allows better characterization and adaptability to each individual cluster. Experiments on both toy and real data sets demonstrate the effectiveness of the proposed FCMK-FS algorithm.
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收藏
页数:8
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