Low-rank embedding for robust image feature extraction

Wai Keung Wong, Zhihui Lai, Jiajun Wen, Xiaozhao Fang, Yuwu Lu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

122 Citations (Scopus)


Robustness to noises, outliers, and corruptions is an important issue in linear dimensionality reduction. Since the sample-specific corruptions and outliers exist, the class-special structure or the local geometric structure is destroyed, and thus, many existing methods, including the popular manifold learning- based linear dimensionality methods, fail to achieve good performance in recognition tasks. In this paper, we focus on the unsupervised robust linear dimensionality reduction on corrupted data by introducing the robust low-rank representation (LRR). Thus, a robust linear dimensionality reduction technique termed low-rank embedding (LRE) is proposed in this paper, which provides a robust image representation to uncover the potential relationship among the images to reduce the negative influence from the occlusion and corruption so as to enhance the algorithm's robustness in image feature extraction. LRE searches the optimal LRR and optimal subspace simultaneously. The model of LRE can be solved by alternatively iterating the argument Lagrangian multiplier method and the eigendecomposition. The theoretical analysis, including convergence analysis and computational complexity, of the algorithms is presented. Experiments on some well-known databases with different corruptions show that LRE is superior to the previous methods of feature extraction, and therefore, it indicates the robustness of the proposed method. The code of this paper can be downloaded from http://www.scholat.com/laizhihui.
Original languageEnglish
Article number7893780
Pages (from-to)2905-2917
Number of pages13
JournalIEEE Transactions on Image Processing
Issue number6
Publication statusPublished - 1 Jun 2017


  • image feature extraction
  • low rank representation
  • Robust linear dimensionality reduction
  • subspace learning

ASJC Scopus subject areas

  • Software
  • General Medicine
  • Computer Graphics and Computer-Aided Design


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