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

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
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