Representing Data by Sparse Combination of Contextual Data Points for Classification

  • Jingyan Wang
  • Yihua Zhou
  • Ming Yin
  • Shaochang Chen
  • Benjamin Edwards
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)


In this paper, we study the problem of using contextual data points of a data point for its classification problem. We propose to represent a data point as the sparse linear reconstruction of its context, and learn the sparse context to gather with a linear classifier in a supervised way to increase its discriminative ability. We proposed a novel formulation for context learning, by modeling the learning of context reconstruction coefficients and classifier in a unified objective. In this objective, the reconstruction error is minimized and the coefficient sparsity is encouraged. Moreover, the hinge loss of the classifier is minimized and the complexity of the classifier is reduced. This objective is optimized by an alternative strategy in an iterative algorithm. Experiments on three benchmark data set show its advantage over state-of-the-art context-based data representation and classification methods.


Pattern classification Context learning Nearest neighbors Sparse regularization 


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Authors and Affiliations

  • Jingyan Wang
    • 1
    • 2
    • 3
  • Yihua Zhou
    • 4
  • Ming Yin
    • 5
  • Shaochang Chen
    • 5
  • Benjamin Edwards
    • 6
  1. 1.National Time Service CenterChinese Academy of SciencesXi’ anChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina
  3. 3.Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouChina
  4. 4.Department of Mechanical Engineering and MechanicsLehigh UniversityBethlehemUSA
  5. 5.Electronic Engineering CollegeNaval University of EngineeringWuhanChina
  6. 6.Department of Computer ScienceSam Houston State UniversityHuntsvilleUSA

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