Knowledge Commons of Institute of Automation,CAS
Global and Local Consistent Multi-view Subspace Clustering | |
Yanbo Fan2; Ran He(赫然)1,2,3; Baogang Hu2 | |
2015 | |
会议名称 | IAPR Asian Conference on Pattern Recognition (ACPR) |
会议日期 | 2015-11 |
会议地点 | Kuala Lumpur, Malaysia |
摘要 |
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. |
关键词 | Multi-view Subspace Clustering |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/20913 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
作者单位 | 1.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 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yanbo Fan,Ran He,Baogang Hu. Global and Local Consistent Multi-view Subspace Clustering[C],2015. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
ACPR2015.pdf(1158KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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