Subspace Learning Based Low-Rank Representation

  • Kewei TangEmail author
  • Xiaodong Liu
  • Zhixun SuEmail author
  • Wei Jiang
  • Jiangxin Dong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10111)


Subspace segmentation has been a hot topic in the past decades. Recently, spectral-clustering based methods arouse broad interests, however, they usually consider the similarity extraction in the original space. In this paper, we propose subspace learning based low-rank representation to learn a subspace favoring the similarity extraction for the low-rank representation. The process of learning the subspace and achieving the representation is conducted simultaneously and thus they can benefit from each other. After extending the linear projection to nonlinear mapping, our method can handle manifold clustering problem which is a general case of subspace segmentation. Moreover, our method can also be applied in the problem of recognition by adding suitable penalty on the learned subspace. Extensive experimental results confirm the effectiveness of our method.


Principle Component Analysis Near Neighbor Affinity Matrix Dimensionality Reduction Method Original Data Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work of K. Tang was supported by the Educational Commission of Liaoning Province, China (No. L201683662). The work of Z. Su was supported by the National Natural Science Foundation of China (No. 61572099), National Science and Technology Major Project (No. 2014ZX04001011, ZX20140419). The work of W. Jiang was supported by the Natural Science Foundation of Liaoning Province, China (No. 60875029).

Supplementary material

416257_1_En_27_MOESM1_ESM.pdf (124 kb)
Supplementary material 1 (pdf 124 KB)


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of MathematicsLiaoning Normal UniversityDalianPeople’s Republic of China
  2. 2.School of Mathematical SciencesDalian University of TechnologyDalianPeople’s Republic of China

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