Computer Science ›› 2020, Vol. 47 ›› Issue (6): 92-97.doi: 10.11896/jsjkx.190500074

• Databωe & Big Data & Data Science • Previous Articles     Next Articles

Robust Low Rank Subspace Clustering Algorithm Based on Projection

XING Yu-hua, LI Ming-xing   

  1. College of Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,China
  • Received:2019-05-15 Online:2020-06-15 Published:2020-06-10
  • About author:XING Yu-hua,born in 1966,master,associate professor.His main research interests include IoT communicationtech-nology and so on.
    LI Ming-xing,born in 1993,master.Her main research interests include communication of internet of things and so on.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (51307140)

Abstract: With the advent of the era of big data,how to effectively cluster,analyze and effectively use massive amounts of high-dimensional data has become a hot research topic.When the traditional clustering algorithms are used to process high-dimensional data,the accuracy and stability of the clustering results are low.The subspace clustering algorithm can reduce the feature space of the original data to form different feature subsets,reduce the influence of uncorrelated features between data on clustering results.It can mine the information that is difficult to display in high-dimensional data,and has significant advantages in processing high-dimensional data.Aiming at the limitations of existing graph-based subspace clustering algorithms in dealing with unknown type noise and solving complex convex problems,based on subspace clustering algorithm,combined with spatial projection theory,this paper proposes a projection-based robust low-rank subspace clustering algorithm.Firstly,the original data is projected,the noise of the projection space is eliminated by coding and the missing data is compensated.Then a new method map is used to construct the sparse similarity l2 graph,and finally the subspace clustering is performed on the basis of the l2 graph.The algorithm does not need a priori knowledge of the type of noise,and the l2 graph can well describe the characteristics of high-dimensional data sparsity and spatial dispersion.Three datasets of face recognition are selected as experimental datasets.Firstly,the optimal parameters affecting the clustering effect are determined,and then the algorithm is verified from three aspects:accuracy,robustness and time complexity.The experimental results show that the algorithm has high accuracy,low time complexity and good robustness,when the unknown type of noise is mixed in the datasets of face recognition.

Key words: High dimensional data, Noise, Subspace clustering, Space projection, l2 graph

CLC Number: 

  • TP311
[1]LU L.Combined central and subspace clustering for computer vision applications[C]//International Conference on Machine Learning.ACM,2006:593-600.
[2]LEE M,CHO J,CHOI C H,et al.Procrustean Normal Distribution for Non-rigid Structure from Motion[C]//Computer Vision and Pattern Recognition.IEEE,2013:1280-1287.
[3]BOULT T E,BROWN L G.Factorization-based segmentation of motions[C]//Proceedings of the IEEE Workshop on Visual Motion.IEEE,2002:179-186.
[4]LIU D,JIANG M H,YANG X F,et al.Analyzing documents with Quantum Clustering:A novel pattern recognition algorithm based on quantum mechanics[J].Pattern Recognition Letters,2016,77:8-13.
[5]LU S,CHANG D.An image segmentation method based on dynamic artificial fish swarm algorithm[C]//2012 IEEE 11th International Conference on Signal Processing.IEEE,2012:980-983.
[6]ELHAMIFAR E,VIDAL R.Clustering disjoint subspaces via sparse representation[C]//IEEE International Conference on Acoustics Speech and Signal Processing.IEEE,2010:1926-1929.
[7]WU X,CHEN X M,LI X,et al.Adaptive subspace learning:an iterative approach for document clustering[J].Neural Computing and Applications,2014,25(2):333-342.
[8]BAI J C,LI J C,DAI P F,et al.General parameterized proximal point algorithm with applications in statistical learning[J].International Journal of Computer Mathematics,2019,96(1):199-215.
[9]WU Y,ZHANG Z,HUANG T S,et al.Multibody Grouping via Orthogonal Subspace Decomposition[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR 2001).IEEE,2001:II-252-II-257 vol.2.
[10]YAN J,POLLEFEYS M.A General Framework for Motion Segmentation:Independent,Articulated,Rigid,Non-rigid,Degenerate and Non-degenerate[C]//European Conference on Computer Vision.Springer Berlin Heidelberg,2006:94-106.
[11]LU C Y,MIN H,ZHAO Z Q,et al.Robust and Efficient Subspace Segmentation via Least Squares Regression[C]//European Conference on Compater Vision.Berlin:Springer,2014:347-360.
[12]LIU G,LIN Z,YAN S,et al.Robust recovery of subspace structures by low-rank representation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2012,35(1):171-184.
[13]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.
[14]CHEN J,ZHANG H,MAO H,et al.Symmetric low-rank representation for subspace clustering[J].Neurocomputing,2014,173(P3):1192-1202.
[15]XIA C Q,HAN K,QI Y,et al.A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression Data[J].IEEE/ACM Tran-sactions on Computational Biology and Bioinformatics,2018,15(4):1315-1324.
[16]LUXBURG,ULRIKE.A tutorial on spectral clustering[J].Statistics & Computing,2007,17(4):395-416.
[17]PAPADAKIS N,BUGEAU A.Tracking with occlusions via graph cuts[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2010,33(1):144-157.
[18]HE X,YAN S,HU Y,et al.Face recognition using laplacian faces[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence.2005:328-340.
[19]WANG J,YANG J,YU K,et al.Locality-constrained linear coding for image classification[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.IEEE,2010:3360-3367.
[20]FAVARO P,VIDAL R,RAVICHANDRAN A.A closed form solution to robust subspace estimation and clustering[C]//Computer Vision and Pattern Recognition.IEEE,2011:1801-1807.
[1] DONG Ming-gang, HUANG Yu-yang, JING Chao. K-Nearest Neighbor Classification Training Set Optimization Method Based on Genetic Instance and Feature Selection [J]. Computer Science, 2020, 47(8): 178-184.
[2] LUO Wen-jun, LEI Shuang. Blind Quantum Computation over Noise Channels [J]. Computer Science, 2020, 47(7): 37-41.
[3] GAO Fang-yuan, WANG Xiu-mei. Subspace Clustering Method Based on Block Diagonal Representation and Neighbor Constraint [J]. Computer Science, 2020, 47(7): 66-70.
[4] WU Jing, ZHOU Xian-chun, XU Xin-ju, HUANG Jin. Image Denoising by Mixing 3D Block Matching with Harmonic Filtering in Transform Domain [J]. Computer Science, 2020, 47(7): 130-134.
[5] DING Qing-feng, XI Tao, LIAN Yi-chong, WU Ze-xiang. Antenna Selection for Spatial Modulation Based on Physical Layer Security [J]. Computer Science, 2020, 47(7): 322-327.
[6] CHEN Jin-yin, CHENG Kai-hui and ZHENG Hai-bin. Deep Learning Based Modulation Recognition Method in Low SNR [J]. Computer Science, 2020, 47(6A): 283-288.
[7] CHEN Jin-yin, JIANG Tao and ZHENG Hai-bin. Radio Modulation Recognition Based on Signal-noise Ratio Classification [J]. Computer Science, 2020, 47(6A): 310-317.
[8] LIU Shu-jun, WEI Lai. Block Integration Based Image Clustering Algorithm [J]. Computer Science, 2020, 47(6): 170-175.
[9] LIN Yun, HUANG Zhen-hang, GAO Fan. Diffusion Maximum Correntropy Criterion Variable Step-size Affine Projection Sign Algorithm [J]. Computer Science, 2020, 47(6): 242-246.
[10] CHEN Pei-pei, LI Tao-shen, FANG Xing, WANG Zhe. Study on Secure Beamforming for Full-duplex Energy Harvesting Relaying System [J]. Computer Science, 2020, 47(6): 316-321.
[11] LI Lan, YANG Chen, WANG An-fu. Study on Selection of Privacy Parameters ε in Differential Privacy Model [J]. Computer Science, 2019, 46(8): 201-205.
[12] MAI Ying-chao,CHEN Yun-hua,ZHANG Ling. Bio-inspired Activation Function with Strong Anti-noise Ability [J]. Computer Science, 2019, 46(7): 206-210.
[13] ZHANG Xu-tao. Filtering Algorithm Based on Gaussian-salt and Pepper Noise [J]. Computer Science, 2019, 46(6A): 263-265.
[14] ZHAO Peng, JIANG Yu-zhong, ZHAI Qi, LI Chun-teng. Improved FCME Algorithm Based on Binary Searching by Mean and Its Applicationsin E/SLF Channel Noise Detection [J]. Computer Science, 2019, 46(6): 118-123.
[15] DONG Qing, LIN Yun. Kernel Fractional Lower Power Adaptive Filtering Algorithm Against Impulsive Noise [J]. Computer Science, 2019, 46(11A): 80-82.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .