Low-rank representation with local constraint for graph construction
Low-rank representation with local constraint for graph construction
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用于图构建的具有局部约束的低秩表示
DOI：
10.1016/j.neucom.2013.06.013
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发表时间：
2013-12
期刊：
影响因子：
6
通讯作者：
Licheng Jiao
中科院分区：
文献类型：
--
作者：
Yaoguo Zheng;Xiangrong Zhang;Shuyuan Yang;Licheng Jiao
基于图的半监督学习近年来得到了广泛的研究。本文提出了一种新颖的具有局部约束的低秩表示（LRRLC）方法来构建图。 LRRLC是在原始的低秩表示（LRR）算法的基础上结合数据的局部信息推导出来的。排名约束能够捕获数据的全局结构。因此，LRRLC 能够同时通过 LRR 捕获全局结构和通过局部约束正则化项捕获局部结构。正则化项是由相似样本具有大相似系数的局部性假设引起的。本文通过LRR获得所有样本之间的相似度度量。考虑到物理解释中系数的非负性限制，正则化项可以写为加权ℓ 1-范数。然后基于局部和全局一致性的半监督学习框架用于分类任务。实验结果表明，与真实人脸和数字数据库上最先进的图相比，LRRLC 算法提供了更好的数据结构表示，并实现了更高的分类精度。
Graph-based semi-supervised learning has been widely researched in recent years. A novel Low-Rank Representation with Local Constraint (LRRLC) approach for graph construction is proposed in this paper. The LRRLC is derived from the original Low-Rank Representation (LRR) algorithm by incorporating the local information of data. Rank constraint has the capacity to capture the global structure of data. Therefore, LRRLC is able to capture both the global structure by LRR and the local structure by the locally constrained regularization term simultaneously. The regularization term is induced by the locality assumption that similar samples have large similarity coefficients. The measurement of similarity among all samples is obtained by LRR in this paper. Considering the non-negativity restriction of the coefficients in physical interpretation, the regularization term can be written as a weighted ℓ 1-norm. Then a semi-supervised learning framework based on local and global consistency is used for the classification task. Experimental results show that the LRRLC algorithm provides better representation of data structure and achieves higher classification accuracy in comparison with the state-of-the-art graphs on real face and digit databases.
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影响因子：
3.2
作者：
Abdi, Herve;Williams, Lynne J.
通讯作者：
Williams, Lynne J.
DOI：
10.1109/cvpr.2012.6247944
发表时间：
2012-06
期刊：
2012 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
Liansheng Zhuang;Haoyuan Gao;Zhouchen Lin;Yi Ma;Xin Zhang;Nenghai Yu
通讯作者：
Liansheng Zhuang;Haoyuan Gao;Zhouchen Lin;Yi Ma;Xin Zhang;Nenghai Yu
影响因子：
3.7
作者：
M. Timmerman
通讯作者：
M. Timmerman
DOI：
10.1145/1557019.1557063
发表时间：
2009-06
期刊：
--
影响因子：
--
作者：
Quanquan Gu;Jie Zhou
通讯作者：
Quanquan Gu;Jie Zhou
DOI：
10.1142/9789812791764_0009
发表时间：
2008-03
期刊：
--
影响因子：
--
作者：
Martin Sewell
通讯作者：
Martin Sewell