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Title:
maximum volume clustering
Author: Gang Niu ; Bo Dai ; Lin Shang ; Masashi Sugiyama
Source: Journal of Machine Learning Research
Conference Name: 14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011
Conference Date: April 11, 2011 - April 13, 2011
Issued Date: 2011
Conference Place: Fort Lauderdale, FL, United states
Keyword: Artificial intelligence ; Clustering algorithms ; Equivalence classes ; Sampling
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
Sponsorship: Google; Microsoft; PASCAL2; IBM; NEC - Epowered by Innovation
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.
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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16311
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Gang Niu,Bo Dai,Lin Shang,et al. maximum volume clustering[C]. 见:14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011. Fort Lauderdale, FL, United states. April 11, 2011 - April 13, 2011.
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