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| a novel multiple kernel clustering method | |
| Zhang Lujiang; Hu Xiaohui | |
| 2012 | |
| Conference Name | 8th International Conference on Emerging Intelligent Computing Technology and Applications, ICIC 2012 |
| Source | Communications in Computer and Information Science |
| Pages | 87-92 |
| Conference Date | July 25, 2012 - July 29, 2012 |
| Conference Place | Huangshan, China |
| 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 |
| 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.; 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. |
| Keyword | Intelligent Computing |
| Sponsorship | IEEE Computational Intelligence Society; International Neural Network Society; National Science Foundation of China |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15801 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Zhang Lujiang,Hu Xiaohui. a novel multiple kernel clustering method[C],2012:87-92. |
| Files in This Item: | There are no files associated with this item. | |||||
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