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	Robust subspace segmentation with block-diagonal prior
	Feng, Jiashi ; Lin, Zhouchen ; Xu, Huan ; Yan, Shuicheng
	2014
英文摘要	The subspace segmentation problem is addressed in this paper by effectively constructing an exactly block-diagonal sample affinity matrix. The block-diagonal structure is heavily desired for accurate sample clustering but is rather difficult to obtain. Most current state-of-the-art subspace segmentation methods (such as SSC[4] and LRR[12]) resort to alternative structural priors (such as sparseness and low-rankness) to construct the affinity matrix. In this work, we directly pursue the block-diagonal structure by proposing a graph Laplacian constraint based formulation, and then develop an efficient stochastic subgradient algorithm for optimization. Moreover, two new subspace segmentation methods, the block-diagonal SSC and LRR, are devised in this work. To the best of our knowledge, this is the first research attempt to explicitly pursue such a block-diagonal structure. Extensive experiments on face clustering, motion segmentation and graph construction for semi-supervised learning clearly demonstrate the superiority of our novelly proposed subspace segmentation methods. ? 2014 IEEE.; EI; CPCI-S(ISTP); 0
语种	英语
DOI标识	10.1109/CVPR.2014.482
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/412519]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Feng, Jiashi,Lin, Zhouchen,Xu, Huan,et al. Robust subspace segmentation with block-diagonal prior. 2014-01-01.

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