Neural Processing Letters

, Volume 50, Issue 2, pp 1035–1050 | Cite as

An Improved Structured Low-Rank Representation for Disjoint Subspace Segmentation

  • Lai WeiEmail author
  • Yan Zhang
  • Jun Yin
  • Rigui Zhou
  • Changming Zhu
  • Xiafeng Zhang


Low-rank representation (LRR) and its extensions have shown prominent performances in subspace segmentation tasks. Among these algorithms, structured-constrained low-rank representation (SCLRR) is proved to be superior to classical LRR because of its usage of structure information of data sets. Compared with LRR, in the objective function of SCLRR, an additional constraint term is added to compel the obtained coefficient matrices to reveal the subspace structures of data sets more precisely. However, it is very difficult to determine the best value for the corresponding parameter of the constraint term, and an improper value will decrease the performance of SCLRR sharply. For the sake of alleviating the problem in SCLRR, in this paper, we proposed an improved structured low-rank representation (ISLRR). Our proposed method introduces the structure information of data sets into the equality constraint term of LRR. Hence, ISLRR avoids the adjustment of the extra parameter. Experiments conducted on some benchmark databases showed that the proposed algorithm was superior to the related algorithms.


Subspace segmentation Spectral clustering Low-rank representation Structure constraint 



The authors would like to thank the anonymous reviewers for their constructive comments on this paper.

Compliance with Ethical Standards

Conflict of interest

The authors declared that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceShanghai Maritime UniversityShanghaiChina

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