CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Global and Local Consistent Multi-view Subspace Clustering
Yanbo Fan2; Ran He(赫然)1,2,3; Baogang Hu2
Conference NameIAPR Asian Conference on Pattern Recognition (ACPR)
Conference Date2015-11
Conference PlaceKuala Lumpur, Malaysia
Multi-view clustering aims to cluster data with multiple sources of information. Comparing with single view clustering, it is challenging to make use of the extra information embedded in multiple views. This paper presents a multi-view subspace clustering method (MSC-GL) by simultaneously combining both the global low-rank constraint and local cross topology preserving constraints. The global constraint maximizes the correlation between representational matrices while encouraging each of them to be low rank. The local constraints enable representational matrices under different views to share local structure information. An efficiently iterative algorithm is developed to minimize the proposed joint learning problem, and extensive experiments on five multi-view benchmarks demonstrate that the proposed model outperforms the state-of-the-art multi-view clustering methods. 
KeywordMulti-view Subspace Clustering
Document Type会议论文
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
Recommended Citation
GB/T 7714
Yanbo Fan,Ran He,Baogang Hu. Global and Local Consistent Multi-view Subspace Clustering[C],2015.
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