Nuclear Norm-Based 2DLPP for Image Classification

Yuwu Lu, Chun Yuan, Zhihui Lai, Xuelong Li, Wai Keung Wong, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)


Two-dimensional locality preserving projections (2DLPP) that use 2D image representation in preserving projection learning can preserve the intrinsic manifold structure and local information of data. However, 2DLPP is based on the Euclidean distance, which is sensitive to noise and outliers in data. In this paper, we propose a novel locality preserving projection method called nuclear norm-based two-dimensional locality preserving projections (NN-2DLPP). First, NN-2DLPP recovers the noisy data matrix through low-rank learning. Second, noise in data is removed and the learned clean data points are projected on a new subspace. Without the disturbance of noise, data points belonging to the same class are kept as close to each other as possible in the new projective subspace. Experimental results on six public image databases with face recognition, object classification, and handwritten digit recognition tasks demonstrated the effectiveness of the proposed method.
Original languageEnglish
Article number7924357
Pages (from-to)2391-2403
Number of pages13
JournalIEEE Transactions on Multimedia
Issue number11
Publication statusPublished - 1 Nov 2017


  • Image classification
  • preserving projections
  • robust
  • two-dimensional

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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