[1]LIN Dahua,YANG Lifeng,DENG Zhenyun,et al.Sparse sample self-representation for subspace clustering[J].CAAI Transactions on Intelligent Systems,2016,11(5):696-702.[doi:10.11992/tis.201601005]
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Sparse sample self-representation for subspace clustering

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 5 Page number: 696-702 Column: 学术论文—机器学习 Public date: 2016-11-01
Title:
Sparse sample self-representation for subspace clustering
Author(s):
LIN Dahua1;  YANG Lifeng2;  DENG Zhenyun2;  LI Yonggang2;  LUO Yan2
1. Guangxi Center for Educational Technology, Nanning 530022, China;
2. Guangxi Key Lab of Multi-source Information Mining & Security, Guilin 541004, China
Keywords:
subspace clustering; spectral clustering; subspace structure; similarity matrix; sample self-representation
CLC:
TP181
DOI:
10.11992/tis.201601005
Abstract:
Existing subspace clustering methods do not combine sample self-representation well with affinity matrix sparsity, for example, by removing disturbances from noise, outliers, etc., when constructing the affinity matrix. This paper proposes a novel subspace clustering method called sparse sample self-representation for subspace clustering. This method fully considers the correlation between the samples, and also takes advantage of L1-norm and L2,1-norm terms to "penalize" the affinity matrix; that is, it conducts sparse sample self-representation for all test samples, to guarantee every sample can be expressed by any other samples with strong similarity and make it more robust. The experimental results of the Hopkins155 dataset and some facial image datasets show that the proposed method outperforms the LSR, SSC, and LRR methods in terms of the subspace clustering error.
References:
[1] 王卫卫, 李小平, 冯象初, 等. 稀疏子空间聚类综述[J]. 自动化学报, 2015, 41(8):1373-1384. WANG Weiwei, LI Xiaoping, FENG Xiangchu, et al. A survey on sparse subspace clustering[J]. Acta automatica sinica, 2015, 41(8):1373-1384.
[2] VIDAL R, MA Yi, SASTRY S. Generalized principal component analysis (GPCA)[J]. IEEE transactions on pattern analysis and machine intelligence, 2005, 27(12):1945-1959.
[3] TSENG P. Nearest q-flat to m points[J]. Journal of optimization theory and applications, 2000, 105(1):249-252.
[4] FISCHLER M A, BOLLES R C. Random sample consensus:a paradigm for model fitting with applications to image analysis and automated cartography[J]. Communications of the ACM, 1981, 24(6):381-395.
[5] 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.
[6] 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, 2013, 35(1):171-184.
[7] LU Canyi, MIN Hai, ZHAO Zhongqiu, et al. Robust and efficient subspace segmentation via least squares regression[C]//Proceedings of the 12th European Conference on Computer Vision. Berlin Heidelberg, 2012:347-360.
[8] FENG Jiashi, LIN Zhouchen, XU Huan, et al. Robust subspace segmentation with block-diagonal prior[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014:3818-3825.
[9] 欧阳佩佩, 赵志刚, 刘桂峰. 一种改进的稀疏子空间聚类算法[J]. 青岛大学学报:自然科学版, 2014, 27(3):44-48. OUYANG Peipei, ZHAO Zhigang, LIU Guifeng. An improved sparse subspace clustering algorithm[J]. Journal of Qingdao university:natural science edition, 2014, 27(3):44-48.
[10] 杨国亮, 罗璐, 丰义琴, 等. 基于低秩稀疏图的结构保持投影算法[J]. 计算机工程与科学, 2015, 37(8):1584-1590. YANG Guoliang, LUO Lu, FENG Yiqin, et al. Structure preserving projection algorithm based on low rank and sparse graph[J]. Computer engineering and science, 2015, 37(8):1584-1590.
[11] ZHU Xiaofeng, HUANG Zi, YANG Yang, et al. Self-taught dimensionality reduction on the high-dimensional small-sized data[J]. Pattern recognition, 2013, 46(1):215-229.
[12] ZHANG Shichao, QIN Zhenxing, LING C X, et al. "Missing is useful":missing values in cost-sensitive decision trees[J]. IEEE transactions on knowledge and data engineering, 2005, 17(12):1689-1693.
[13] ZHU Xiaofeng, HUANG Zi, CUI Jiangtao, et al. Video-to-shot tag propagation by graph sparse group lasso[J]. IEEE transactions on multimedia, 2013, 15(3):633-646.
[14] ZHU Xiaofeng, ZHANG Lei, HUANG Zi. A sparse embedding and least variance encoding approach to hashing[J]. IEEE transactions on image processing, 2014, 23(9):3737-3750.
[15] TRON R, VIDAL R. A benchmark for the comparison of 3-D motion segmentation algorithms[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Minneapolis, MN, USA, 2007:1-8.
[16] LYONS M, AKAMATSU S, KAMACHI M, et al. Coding facial expressions with gabor wavelets[C]//Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition. Nara, 1998:200-205.
[17] HULL J J. A database for handwritten text recognition research[J]. IEEE transactions on pattern analysis and machine intelligence, 1994, 16(5):550-554.
[18] SAMARIA F S, HARTERT A C. Parameterisation of a stochastic model for human face identification[C]//Proceedings of the 2nd IEEE Workshop on Applications of Computer Vision. Sarasota, FL, USA, 1994:138-142.
[19] FENG Jiashi, LIN Zhouchen, XU Huan, et al. Robust subspace segmentation with block-diagonal prior[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA, 2014:3818-3825.
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