A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification

Zhengming Li, Zhihui Lai, Yong Xu, Jian Yang, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

137 Citations (Scopus)


Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.
Original languageEnglish
Pages (from-to)278-293
Number of pages16
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number2
Publication statusPublished - 1 Feb 2017


  • Dictionary learning
  • label embedding
  • locality constrained
  • profile
  • sparse coding

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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