Semi-supervised classification via kernel low-rank representation graph
Semi-supervised classification via kernel low-rank representation graph
复制标题
通过内核低秩表示图进行半监督分类
DOI：
10.1016/j.knosys.2014.06.007
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发表时间：
2014-10
影响因子：
8.8
通讯作者：
Jiao, Licheng
中科院分区：
文献类型：
--
作者：
Feng, Zhixi;Ren, Yu;Liu, Hongying;Jiao, Licheng
基于稀疏表示的图（SRG）近年来引起了越来越多的关注。然而，由于缺乏全局约束的解决方案，稀疏表示，SRG不能准确地揭示数据结构时，数据严重损坏。为了在只有少量标记样本的情况下实现对大范围数据集的鲁棒分类，将低秩表示（LRR）与图和核技巧相结合，提出了一种新的半监督核低秩表示图（SKLRG）.首先学习核投影以找到数据具有可能的低秩结构的高维空间。然后计算投影数据的低秩表示，从中我们可以导出SKLRG矩阵来评估数据亲和度并对损坏的模式进行分类。该方法能自然地揭示投影空间中数据之间的关系，并能捕捉复杂数据的全局结构，实现更鲁棒的子空间分割。此外，SKLRG的连接权重通过成对约束进行细化，其中标签信息被探索以进一步改善分类结果。在一些基准数据集和受相干斑噪声污染的合成孔径雷达（SAR）图像上进行了实验。实验结果表明，当标记样本数量较少时，SKLRG算法可以获得更好的性能。
Sparse Representation based Graphs (SRGs) have attracted increasing interests in very recent years. However, for lacking global constraints on solutions to sparse representation, SRGs cannot accurately reveal data structure when data are grossly corrupted. In this paper, in order to achieve robust classification of wide range of datasets when only a small number of labeled samples are available, we advance a new semi-supervised kernel low-rank representation graph (SKLRG), by combining low-rank representation (LRR) with graphs and kernel trick. A kernel projection is first learned to find high-dimensional space where data have possible low-rank structure. Then a low-rank representation of the projected data is calculated from which we can derive a SKLRG matrix to evaluate data affinity and classify corrupted patterns. The proposed SKLRG can naturally reveal the relationship among data in the projected space, and can capture the global structure of complex data and implements more robust subspace segmentation. Moreover, connected weights of SKLRG are refined by pairwise constrains where label information is explored to further improve the classification results. Some experiments are taken on some benchmark datasets and Synthetic Aperture Radar (SAR) images that are corrupted by speckle noise. The results show that the proposed SKLRG can achieve better performance than its counterparts when there are only a small number of labeled samples.
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影响因子：
10.6
作者：
Cheng, Bin;Yang, Jianchao;Huang, Thomas S.
通讯作者：
Huang, Thomas S.
影响因子：
8.9
作者：
Xin Zhang;F. Sun;Guangcan Liu;Yi Ma
通讯作者：
Xin Zhang;F. Sun;Guangcan Liu;Yi Ma
DOI：
10.1109/isda.2006.253724
发表时间：
2006-10
期刊：
Sixth International Conference on Intelligent Systems Design and Applications
影响因子：
--
作者：
Rong Liu;Jian-zhong Zhou;Ming Liu
通讯作者：
Rong Liu;Jian-zhong Zhou;Ming Liu
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
用于图构建的具有局部约束的低秩表示
影响因子：
6
作者：
Yaoguo Zheng;Xiangrong Zhang;Shuyuan Yang;Licheng Jiao
通讯作者：
Licheng Jiao