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Two-Step Greedy Subspace Clustering
Lingxiao Song1,2; Man Zhang1,2; Zhenan Sun1,2; Jian Liang1,2; Ran He(赫然)1,2; Song, Lingxiao
2015
Conference NamePacific-Rim Conference on Multimedia (PCM)
Source PublicationLecture Notes in Computer Science
Conference Date2015-9
Conference PlaceGwangju, Korea
AbstractGreedy subspace clustering methods provide an efficient way to cluster large-scale multimedia datasets. However, these methods do not guarantee a global optimum and their clustering performance mainly depends on their initializations. To alleviate this initialization problem, this paper proposes a two-step greedy strategy by exploring proper neighbors that span an initial subspace. Firstly, for each data point, we seek a sparse representation with respect to its nearest neighbors. The data points corresponding to nonzero entries in the learning representation form an initial subspace, which potentially rejects bad or redundant data points. Secondly, the subspace is updated by adding an orthogonal basis involved with the newly added data points. Experimental results on real-world applications demonstrate that our method can significantly improve the clustering accuracy of greedy subspace clustering methods without scarifying much computational time.
KeywordGreedy Subspace Clustering Sparse Representation Subspace Neighbor
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11620
Collection智能感知与计算研究中心
Corresponding AuthorSong, Lingxiao
Affiliation1.Center for Research on Intelligent Perception and Computing
2.Institute of Automation, Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Lingxiao Song,Man Zhang,Zhenan Sun,et al. Two-Step Greedy Subspace Clustering[C],2015.
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