Local consistent low rank representation for image clustering

  title={Local consistent low rank representation for image clustering},
  author={Yuqing Shi and Shiqiang Du and Weilan Wang},
  journal={2016 Chinese Control and Decision Conference (CCDC)},
Low rank representation (LRR) is one of the state-of-the-art methods for subspace clustering, which has been widely used in machine learning, data mining, and pattern recognition. The main objective of LRR is to seek the lowest rank representations for the data points based on a given dictionary. However, there are some drawbacks in the current LRR-based approaches: 1) the original data usually contain noise and may not be representative as a dictionary; 2) only global Euclidean structure is… CONTINUE READING


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et al

  • P. Zhu, W. Zuo, L. Zhang
  • Unsupervised feature selection by regularized…
  • 2015
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