Multiple kernel sparse representations for supervised and unsupervised learning

Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods.

Original languageEnglish (US)
Article number6813695
Pages (from-to)2905-2915
Number of pages11
JournalIEEE Transactions on Image Processing
Issue number7
StatePublished - Jul 2014


  • Sparse coding
  • clustering
  • dictionary learning
  • multiple kernel learning
  • object recognition

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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