Structurally incoherent low-rank 2DLPP for image classification

Yuwu Lu, Chun Yuan, Xuelong Li, Zhihui Lai, David Zhang, Linlin Shen

Research output: Journal PublicationArticlepeer-review

18 Citations (Scopus)


Preserving projection-based methods are good for finding the manifold structure embedded in data. As they use the Euclidean distance as a metric, which is sensitive to noise and outliers in data, nuclear norm-based 2D locality preserving projection (NN-2DLPP) is thus proposed to improve the robustness of 2DLPP. However, NN-2DLPP does not consider the discriminant ability of data. In order to improve the discriminant ability of preserving projection methods, in this paper, we use preserving projection learning with structurally incoherence of data and propose structurally incoherent low-rank 2DLPP (SILR-2DLPP) for image classification. This approach provides a discriminative representation of preserving projection learning by recovering the distinct different classes of the data. SILR-2DLPP searches the optimal subspace and low-rank representation simultaneously. We further extend SILR-2DLPP to a kernel case and propose kernel SILR-2DLPP (KSILR-2DLPP) to obtain a nonlinear representation. The theoretical analysis including the convergence and computational complexity of SILR-2DLPP are presented. To verify the performance of SILR-2DLPP and KSILR-2DLPP, six well-known image databases were used in the experiments. The experimental results show that the proposed methods are superior to the previous preserving projection methods for image classification.

Original languageEnglish
Article number8392778
Pages (from-to)1701-1714
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number6
Publication statusPublished - Jun 2019
Externally publishedYes


  • Feature extraction
  • LPP
  • Low-rank
  • Robust
  • Structurally incoherent

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


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