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	Constrained clustering via spectral regularization
	Zhenguo Li; Jianzhuang Liu; Xiaoou Tang
	2009
会议名称	2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
会议地点	Sanya, China
英文摘要	We propose a novel framework for constrained spectral clustering with pairwise constraints which specify whether two objects belong to the same cluster or not. Unlike previous methods that modify the similarity matrix with pairwise constraints, we adapt the spectral embedding towards an ideal embedding as consistent with the pairwise constraints as possible. Our formulation leads to a small semidefinite program whose complexity is independent of the number of objects in the data set and the number of pairwise constraints, making it scalable to large-scale problems. The proposed approach is applicable directly to multi-class problems, handles both must-link and cannotlink constraints, and can effectively propagate pairwise constraints. Extensive experiments on real image data and UCI data have demonstrated the efficacy of our algorithm.
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/2390]
专题	深圳先进技术研究院_集成所
作者单位	2009
推荐引用方式 GB/T 7714	Zhenguo Li,Jianzhuang Liu,Xiaoou Tang. Constrained clustering via spectral regularization[C]. 见:2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. Sanya, China.

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