An integrated optimisation algorithm for feature extraction, dictionary learning and classification

Yan Cui, Jielin Jiang, Zhihui Lai, Wai Keung Wong, Zuojin Hu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


Recently, sparse representation-based classification (SRC) has received much attention for its robustness in pattern recognition. Because SRC deals with high-dimensional data, huge computational resources are required to compute sparse representations of query samples, which renders SRC solutions of high-dimensional problems infeasible. To overcome this problem, an integrated optimisation algorithm is proposed to implement feature extraction, dictionary learning and classification simultaneously. First, to obtain sparse representation coefficients, a sparsity preserving embedding map is learnt to reduce the dimensionality of the data. Second, an optimal dictionary is adaptively obtained from the training data to reduce trivial information. Third, the training samples are reclassified using sparse representation coefficients. Furthermore, the integrated learning algorithm is extended to unsupervised learning. Experimental results clearly demonstrate that the proposed method achieves better performance than several popular feature extraction and classification methods.
Original languageEnglish
Pages (from-to)2740-2751
Number of pages12
Publication statusPublished - 31 Jan 2018


  • Dictionary optimisation
  • Feature extraction
  • Sparse representation
  • Supervised learning
  • Unsupervised learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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