maximum volume clustering

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	maximum volume clustering
	Gang Niu ; Bo Dai ; Lin Shang ; Masashi Sugiyama
	2011
会议名称	14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011
会议日期	April 11, 2011 - April 13, 2011
会议地点	Fort Lauderdale, FL, United states
关键词	Artificial intelligence Clustering algorithms Equivalence classes Sampling
页码	561-569
中文摘要	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.
英文摘要	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.
收录类别	EI
会议主办者	Google; Microsoft; PASCAL2; IBM; NEC - Epowered by Innovation
会议录	Journal of Machine Learning Research
语种	英语
ISSN号	1532-4435
内容类型	会议论文
源URL	[http://ir.iscas.ac.cn/handle/311060/16311]
专题	软件研究所_软件所图书馆_会议论文
推荐引用方式 GB/T 7714	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.