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Float Greedy-search-based Subspace Clustering
Lingxiao Ling1,2; Man Zhang1,2; Qi Li1,2; Zhenan Sun1,2,3; Ran He(赫然)1,2,3; Song, Lingxiao
Conference NameAsian Conference on Pattern Recognition (ACPR)
Source PublicationProceedings of the IAPR Asian Conference on Pattern Recognition
Conference Date2015-11
Conference PlaceKuala Lumpur, Malaysia
AbstractMany kinds of efficient greedy subspace clustering methods have been proposed to cut down the computation time in clustering large-scale multimedia datasets. However, these methods are easy to fall into local optimum due to the inherent characteristic of greedy algorithms, which are stepoptimal only. To alleviate this problem, this paper proposes a novel greedy subspace clustering strategy based on floating search, called Float Greedy Subspace Clustering (FloatGSC). In order to control the complexity, the nearest subspace neighbor is added in a greedy way, and the subspace is updated by adding an orthogonal basis involved with the newly added data points in each iteration. Besides, a backtracking mechanism is introduced after each iteration to reject wrong neighbors selected in previous iterations. Extensive experiments on motion segmentation and face clustering show that our algorithm can significantly improve the clustering accuracy without sacrificing much computational time, compared with previous greedy subspace clustering methods.
KeywordSubspace Clustering Floating Search
Indexed ByEI
Document Type会议论文
Corresponding AuthorSong, Lingxiao
Affiliation1.Center for Research on Intelligent Perception and Computing, CASIA
2.National Laboratory of Pattern Recognition, CASIA
3.Center for Excellence in Brain Science and Intelligence Technology, CAS
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Lingxiao Ling,Man Zhang,Qi Li,et al. Float Greedy-search-based Subspace Clustering[C],2015.
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