Self-supervised learning based on discriminative nonlinear features for image classification

Qi Tian*, Ying Wu, Jie Yu, Thomas S. Huang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


For learning-based tasks such as image classification, the feature dimension is usually very high. The learning is afflicted by the curse of dimensionality as the search space grows exponentially with the dimension. Discriminant-EM (DEM) proposed a framework by applying self-supervised learning in a discriminating subspace. This paper extends the linear DEM to a nonlinear kernel algorithm, Kernel DEM (KDEM), and evaluates KDEM extensively on benchmark image databases and synthetic data. Various comparisons with other state-of-the-art learning techniques are investigated for several tasks of image classification. Extensive results show the effectiveness of our approach.

Original languageEnglish (US)
Pages (from-to)903-917
Number of pages15
JournalPattern Recognition
Issue number6
StatePublished - Jun 2005


  • Discriminant analysis
  • Image classification
  • Kernel function
  • Support vector machine
  • Unlabeled data

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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