Low-Rank Preserving Projections

Yuwu Lu, Zhihui Lai, Yong Xu, Xuelong Li, Dapeng Zhang, Chun Yuan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

129 Citations (Scopus)


As one of the most popular dimensionality reduction techniques, locality preserving projections (LPP) has been widely used in computer vision and pattern recognition. However, in practical applications, data is always corrupted by noises. For the corrupted data, samples from the same class may not be distributed in the nearest area, thus LPP may lose its effectiveness. In this paper, it is assumed that data is grossly corrupted and the noise matrix is sparse. Based on these assumptions, we propose a novel dimensionality reduction method, named low-rank preserving projections (LRPP) for image classification. LRPP learns a low-rank weight matrix by projecting the data on a low-dimensional subspace. We use the L21norm as a sparse constraint on the noise matrix and the nuclear norm as a low-rank constraint on the weight matrix. LRPP keeps the global structure of the data during the dimensionality reduction procedure and the learned low rank weight matrix can reduce the disturbance of noises in the data. LRPP can learn a robust subspace from the corrupted data. To verify the performance of LRPP in image dimensionality reduction and classification, we compare LRPP with the state-of-the-art dimensionality reduction methods. The experimental results show the effectiveness and the feasibility of the proposed method with encouraging results.
Original languageEnglish
Article number7182766
Pages (from-to)1900-1913
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number8
Publication statusPublished - 1 Aug 2016


  • Face recognition
  • image classification
  • locality preserving projections (LPP)
  • low-rank representation (LRR)

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Low-Rank Preserving Projections'. Together they form a unique fingerprint.

Cite this