Structure-Constrained Symmetric Low-Rank Representation Algorithm for Subspace Clustering

doi:10.19678/j.issn.1000-3428.0057476

Abstract

Abstract: The potential subspace structure of high-dimensional data can be obtained by using subspace clustering,but the existing methods can not reveal the characteristics of global low-rank structure and local sparse structure of data at the same time,which limits the clustering performance.This paper proposes a Structure-Constrained Symmetric Low-Rank Representation(SCSLR) algorithm for subspace clustering.The structure constraint and symmetry constraint are introduced into the object function to limit the solution structure of low-rank representation,and a weighted sparse and symmetric low-rank affinity graph is constructed.On this basis,the spectrum clustering method is used to realize efficient subspace clustering.Experimental results show that the proposed algorithm can accurately represent the complex subspace structure.Its average clustering error on two benchmark datasets,Extended Yale B and Hopkins 155,is 1.37% and 1.43% respectively,and its clustering performance is better than that of Low-Rank Representation(LRR),Sparse Subspace Clustering(LSS),Structure-Constrained Symmetric LRR(LRRSC) and other algorithms.

Key words: Low-Rank Representation(LRR), sparse representation, weighed constraint, symmetric constraint, subspace clustering

摘要： 通过子空间聚类可获得高维数据的潜在子空间结构，但现有算法不能同时揭示数据全局低秩结构和局部稀疏结构特性，致使聚类性能受限。提出一种结构约束的对称低秩表示算法用于子空间聚类。在目标函数中添加结构约束和对称约束来限制低秩表示解的结构，构造一个加权稀疏和对称低秩的亲和度图，在此基础上，结合谱聚类方法实现高效的子空间聚类。实验结果表明，该算法能够准确表示复杂子空间结构，其在Extended Yale B和Hopkins 155基准数据集上的平均聚类误差分别为1.37%和1.43%，聚类性能优于LRR、SSC、LRRSC等算法。

关键词: 低秩表示, 稀疏表示, 加权约束, 对称约束, 子空间聚类

CLC Number:

TP18

TAO Yang, BAO Linglang, HU Hao. Structure-Constrained Symmetric Low-Rank Representation Algorithm for Subspace Clustering[J]. Computer Engineering, 2021, 47(4): 56-61,67.

陶洋, 鲍灵浪, 胡昊. 结构约束的对称低秩表示子空间聚类算法[J]. 计算机工程, 2021, 47(4): 56-61,67.

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References

[1] TANG Kewei,SU Zhixun,JIANG Wei,et al.Robust subspace learning-based low-rank representation for manifold clustering[J].Neural Computing and Applications,2019,31(11):7921-7933.
[2] ZHAO Xi,QIN Qianqing,LUO Bin.Motion segmentation based on model selection in permutation space for RGB sensors[J].Sensors,2019,19(13):1-15.
[3] GUO Jiping,YIN Wenbin,SUN Yanfeng,et al.Multi-view subspace clustering with block diagonal representa-tion[J].IEEE Access,2019,7:84829-84838.
[4] LIU Guangcan,LIN Zhouchen,YAN Shuicheng,et al.Robust recovery of subspace structures by low-rank representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,35(1):171-184.
[5] GAO J,KANG M,TIAN J,et al.Unsupervised locality-preserving robust latent low-rank recovery-based subspace clustering for fault diagnosis[J].IEEE Access,2018,6:52345-52354.
[6] ARIAS-CASTRO E,LERMAN G,ZHANG T.Spectral clustering based on local PCA[J].The Journal of Machine Learning Research,2017,18(1):253-309.
[7] XU Xiaolong,WANG Shitong,MEI Xiangdong.Improved clustering algorithm based on local and global information[J].Computer Engineering,2015,41(6):165-171.(in Chinese)许小龙,王士同,梅向东.基于局部和全局信息的改进聚类算法[J].计算机工程,2015,41(6):165-171.
[8] LI Xiaohong,XIE Meng,MA Huifang,et al.A short text clustering algorithm based on spectral cut[J].Computer Engineering,2016,42(8):178-182.(in Chinese)李晓红,谢蒙,马慧芳,等.一种基于谱分割的短文本聚类算法[J].计算机工程,2016,42(8):178-182.
[9] ELHAMIFAR E,VIDAL R.Sparse subspace clustering:algorithm,theory,and applications[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(11):2765-2781.
[10] LIU Guangcan,LIN Zhouchen,YU Yong.Robust subspace segmentation by low-rank representation[C]//Proceedings of the 27th International Conference on Machine Learning.Haifa,Israel:[s.n.],2010:663-670.
[11] CHEN Jie,MAO Hua,SANG Yongsheng,et al.Subspace clustering using a symmetric low-rank representation[J].Knowledge-Based Systems,2017,127:46-57.
[12] FANG Xiaozhao,HAN Na,WU Jigang,et al.Approximate low-rank projection learning for feature extraction[J].IEEE Transactions on Neural Networks and Learning Systems,2018,29(11):5228-5241.
[13] WANG Qi,HE Xiang,LI Xuelong.Locality and structure regularized low rank representation for hyperspectral image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2018,57(2):911-923.
[14] NI Yuzhao,SUN Ju,YUAN Xiaotong,et al.Robust low-rank subspace segmentation with semidefinite guarantees[C]//Proceedings of 2010 IEEE International Conference on Data Mining.Washington D.C.,USA:IEEE Press,2010:1179-1188.
[15] ZHUANG Liansheng,GAO Haoyuan,LIN Zhouchen,et al.Non-negative low rank and sparse graph for semi-supervised learning[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2012:2328-2335.
[16] SHI J B,JITENDRA M.Normalized cuts and image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,22(8):888-905.
[17] TANG Kewei,LIU Risheng,SU Zhixun,et al.Structure-constrained low-rank representation[J].IEEE Transactions on Neural Networks and Learning Systems,2014,25(12):2167-2179.
[18] TRON R,VIDAL R.A benchmark for the comparison of 3-D motion segmentation algorithms[C]//Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2007:1-8.
[19] FENG Jiashi,LIN Zhouchen,XU Huan,et al.Robust subspace segmentation with block-diagonal prior[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2014:3818-3825.
[20] YAN J Y,POLLEFEYS M.A general framework for motion segmentation:independent,articulated,rigid,non-rigid,degenerate and non-degenerate[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2006:94-106.
[21] VIDAL R,FAVARO P.Low rank subspace clustering[J].Pattern Recognition Letters,2014,43(1):47-61.

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