The theoretical analysis of GLRAM and its applications

Zhizheng Liang, Dapeng Zhang, Pengfei Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


Matrix-based methods such as two-dimensional principal component analysis (2DPCA) and generalized low rank approximations of matrices (GLRAM) have gained wide attention from researchers due to their computational efficiency. In this paper, we propose a non-iterative algorithm for GLRAM. Firstly, the optimal property of GLRAM is revealed, which is closely related to PCA. Moreover, it also shows that the reconstruction error of GLRAM is not smaller than that of PCA when considering the same dimensionality. Secondly, a non-iterative algorithm for GLRAM is derived. And the proposed method obtains smaller reconstruction error than 2DPCA or GLRAM. Finally, experimental results on face images and handwritten numeral characters show that the proposed method can achieve competitive results with some existing methods such as 2DPCA and PCA in terms of the classification performance or the reconstruction error.
Original languageEnglish
Pages (from-to)1032-1041
Number of pages10
JournalPattern Recognition
Issue number3
Publication statusPublished - 1 Mar 2007


  • 2DPCA
  • Character recognition
  • Face recognition
  • PCA

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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