JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2016, Vol. 51 ›› Issue (12): 108-115.doi: 10.6040/j.issn.1671-9352.0.2016.274

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Pattern shrinking least square regression for subspace segmentation

CHEN Xiao-yun, LIAO Meng-zhen, CHEN Hui-juan   

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, Fujian, China
  • Received:2015-06-06 Online:2016-12-20 Published:2016-12-20

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Abstract: Clustering of gene expression data is an important method to discover the new category of tumor. However, clustering directly on the original gene data will lose the hidden manifold structure information, and then affect the clustering effect of the subspace segmentation method. In order to solve this problem, the pattern shrinking least square regression model for subspace segmentation(PSLSR)is proposed. This model can perform pattern shrinking and learn the affine matrix of data simultaneously, and be solved by using the alternating optimization method. Experimental results on six gene expression data show that PSLSR significantly outperforms the existing subspace segmentation methods.

Key words: gene expression data, pattern shrinking, subspace segmentation, alternative optimization

CLC Number: 

  • TP311
[1] JIANG Daxin, TANG Chun, ZHANG Aidong. Cluster analysis for gene expression data: a survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(11):1370-1386.
[2] VIDAL R. A tutorial on subspace clustering[J]. IEEE Signal Processing Magazine, 2010, 28(2):52-68.
[3] CANDÈS E J, LI Xiaodong, MA Yi, et al. Robust principal component analysis?[J]. Journal of the ACM(JACM), 2011, 58(3):11:1-37.
[4] VIDAL R, FAVARO P. Low rank subspace clustering(LRSC)[J]. Pattern Recognition Letters, 2014, 43:47-61.
[5] HOU Chenping, NIE Feiping, JIAO Yuanyuan, et al. Learning a subspace for clustering via pattern shrinking[J]. Information Processing and Management, 2013, 49(4):871-883.
[6] ZHOU Dengyong, BOUSQUET O, LAL T N, et al. Learning with local and global consistency[J]. Advances in Neural Information Processing Systems, 2004, 16(16):321-328.
[7] ELHAMIFAR E, VIDAL R. Sparse subspace clustering[C] //IEEE Conference on Computer Vision and Pattern Recognition(CVPR 2009). New York: IEEE, 2009: 2790-2797.
[8] LIU Guangcan, LIN Zhouchen, YU Yong. Robust subspace segmentation by low-rank representation[C/OL] //Proceedings of the 27th International Conference on Machine Learning(ICML-10). 2010: 663-670.[2015-02-06].http://icml2010.haifa.il.ibm.com/papers/521.pdf.
[9] LU Canyi, MIN Hai, ZHAO Zhongqiu, et al. Robust and efficient subspace segmentation via least squares regression[M] //FITZGIBBON A, LAZEBNIK S, PERONA P, et al. Lecture Notes in Computer Science. Berlin: Springer, 2012: 347-360.
[10] FAVARO P, VIDAL R, RAVICHANDRAN A. A closed form solution to robust subspace estimation and clustering[C] //IEEE Conference on Computer Vision and Pattern Recognition(CVPR). New York: IEEE, 2011: 1801-1807.
[11] NIYOGI X. Locality preserving projections [M] //THRUN S, SAUL K, SCHOLKOPF B. Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2004, 16:153-160.
[12] CAI Den, HE Xiaofei, WU Xiaoyun, et al. Non-negative matrix factorization on manifold[C] //IEEE International Conference on Data Mining(ICDM2008). Los Alamitos: IEEE Computer Society, 2008: 63-72.
[13] SHI Jianbo, MALIK J. Normalized cuts and image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8):888-905.
[14] YU Lei, DING C, LOSCALZO S. Stable feature selection via dense feature groups[C] //Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data mining. New York: ACM, 2008: 803-811.
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