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Low-Rank Representation for Incomplete Data

Author

Listed:
  • Jiarong Shi
  • Wei Yang
  • Longquan Yong
  • Xiuyun Zheng

Abstract

Low-rank matrix recovery (LRMR) has been becoming an increasingly popular technique for analyzing data with missing entries, gross corruptions, and outliers. As a significant component of LRMR, the model of low-rank representation (LRR) seeks the lowest-rank representation among all samples and it is robust for recovering subspace structures. This paper attempts to solve the problem of LRR with partially observed entries. Firstly, we construct a nonconvex minimization by taking the low rankness, robustness, and incompletion into consideration. Then we employ the technique of augmented Lagrange multipliers to solve the proposed program. Finally, experimental results on synthetic and real-world datasets validate the feasibility and effectiveness of the proposed method.

Suggested Citation

  • Jiarong Shi & Wei Yang & Longquan Yong & Xiuyun Zheng, 2014. "Low-Rank Representation for Incomplete Data," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, December.
    • Handle: RePEc:hin:jnlmpe:439417
    DOI: 10.1155/2014/439417
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    Cited by:

    1. Bin Liu & Weifeng Chen & Bo Li & Xiuping Liu, 2022. "Neural Subspace Learning for Surface Defect Detection," Mathematics, MDPI, vol. 10(22), pages 1-16, November.

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