Max-Margin-Based Discriminative Feature Learning

Changsheng Li, Qingshan Liu, Weishan Dong, Fan Wei, Xin Zhang, Lin Yang

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


In this brief, we propose a new max-margin-based discriminative feature learning method. In particular, we aim at learning a low-dimensional feature representation, so as to maximize the global margin of the data and make the samples from the same class as close as possible. In order to enhance the robustness to noise, we leverage a regularization term to make the transformation matrix sparse in rows. In addition, we further learn and leverage the correlations among multiple categories for assisting in learning discriminative features. The experimental results demonstrate the power of the proposed method against the related state-of-the-art methods.

Original languageEnglish (US)
Article number7398096
Pages (from-to)2768-2775
Number of pages8
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number12
StatePublished - Dec 2016

All Science Journal Classification (ASJC) codes

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


  • Correlation relationship
  • feature learning
  • max-margin
  • row sparsity

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