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| maximum volume clustering | |
| Gang Niu; Bo Dai; Lin Shang; Masashi Sugiyama | |
| 2011 | |
| Conference Name | 14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011 |
| Source | Journal of Machine Learning Research |
| Pages | 561-569 |
| Conference Date | April 11, 2011 - April 13, 2011 |
| Conference Place | Fort Lauderdale, FL, United states |
| Indexed Type | EI |
| ISSN | 1532-4435 |
| Department | (1) Department of Computer Science Tokyo Institute of Technology Japan; (2) State Key Laboratory for Novel Software Technology Nanjing University China; (3) NLPR/LIAMA Institute of Automation Chinese Academy of Science China |
| English Abstract | The large volume principle proposed by Vladimir Vapnik, which advocates that hypotheses lying in an equivalence class with a larger volume are more preferable, is a useful alternative to the large margin principle. In this paper, we introduce a clustering model based on the large volume principle called maximum volume clustering (MVC), and propose two algorithms to solve it approximately: a soft-label and a hard-labelMVC algorithms based on sequential quadratic programming and semi-definite programming, respectively. Our MVC model includes spectral clustering and maximum margin clustering as special cases, and is substantially more general. We also establish the finite sample stability and an error bound for soft-label MVC method. Experiments show that the proposed MVC approach compares favorably with state-of-the-art clustering algorithms. Copyright 2011 by the authors.; The large volume principle proposed by Vladimir Vapnik, which advocates that hypotheses lying in an equivalence class with a larger volume are more preferable, is a useful alternative to the large margin principle. In this paper, we introduce a clustering model based on the large volume principle called maximum volume clustering (MVC), and propose two algorithms to solve it approximately: a soft-label and a hard-labelMVC algorithms based on sequential quadratic programming and semi-definite programming, respectively. Our MVC model includes spectral clustering and maximum margin clustering as special cases, and is substantially more general. We also establish the finite sample stability and an error bound for soft-label MVC method. Experiments show that the proposed MVC approach compares favorably with state-of-the-art clustering algorithms. Copyright 2011 by the authors. |
| Keyword | Artificial Intelligence Clustering Algorithms Equivalence Classes Sampling |
| Sponsorship | Google; Microsoft; PASCAL2; IBM; NEC - Epowered by Innovation |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16311 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Gang Niu,Bo Dai,Lin Shang,et al. maximum volume clustering[C],2011:561-569. |
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