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Title:
a novel multiple kernel clustering method
Author: Zhang Lujiang ; Hu Xiaohui
Source: Communications in Computer and Information Science
Conference Name: 8th International Conference on Emerging Intelligent Computing Technology and Applications, ICIC 2012
Conference Date: July 25, 2012 - July 29, 2012
Issued Date: 2012
Conference Place: Huangshan, China
Keyword: Intelligent computing
Indexed Type: EI
ISSN: 1865-0929
ISBN: 9783642318368
Department: (1) School of Automation Science and Electrical Engineering Beijing University of Aeronautics and Astronautics Beijing China; (2) Institute of Software Chinese Academy of Sciences Beijing China
Sponsorship: IEEE Computational Intelligence Society; International Neural Network Society; National Science Foundation of China
Abstract: Recently Multiple Kernel Learning (MKL) has gained increasing attention in constructing a combinational kernel from a number of basis kernels. In this paper, we proposed a novel approach of multiple kernel learning for clustering based on the kernel k-means algorithm. Rather than using a convex combination of multiple kernels over the whole input space, our method associates to each cluster a localized kernel. We assign to each cluster a weight vector for feature selection and combine it with a Gaussian kernel to form a unique kernel for the corresponding cluster. A locally adaptive strategy is used to localize the kernel for each cluster with the aim of minimizing the within-cluster variance of the corresponding cluster. We experimentally compared our methods to kernel k-means and spectral clustering on several data sets. Empirical results demonstrate the effectiveness of our method. © 2012 Springer-Verlag.
English Abstract: Recently Multiple Kernel Learning (MKL) has gained increasing attention in constructing a combinational kernel from a number of basis kernels. In this paper, we proposed a novel approach of multiple kernel learning for clustering based on the kernel k-means algorithm. Rather than using a convex combination of multiple kernels over the whole input space, our method associates to each cluster a localized kernel. We assign to each cluster a weight vector for feature selection and combine it with a Gaussian kernel to form a unique kernel for the corresponding cluster. A locally adaptive strategy is used to localize the kernel for each cluster with the aim of minimizing the within-cluster variance of the corresponding cluster. We experimentally compared our methods to kernel k-means and spectral clustering on several data sets. Empirical results demonstrate the effectiveness of our method. © 2012 Springer-Verlag.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15801
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Zhang Lujiang,Hu Xiaohui. a novel multiple kernel clustering method[C]. 见:8th International Conference on Emerging Intelligent Computing Technology and Applications, ICIC 2012. Huangshan, China. July 25, 2012 - July 29, 2012.
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