Supervised learning of sparse context reconstruction coefficients for data representation and classification

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Liu X., Wang J., Yin M., Edwards B., Xu P.

NEURAL COMPUTING & APPLICATIONS, vol.28, no.1, pp.135-143, 2017 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 1
  • Publication Date: 2017
  • Doi Number: 10.1007/s00521-015-2042-5
  • Page Numbers: pp.135-143


Context of data points, which is usually defined as the other data points in a data set, has been found to paly important roles in data representation and classification. In this paper, we study the problem of using context of a data point for its classification problem. Our work is inspired by the observation that actually only very few data points are critical in the context of a data point for its representation and classification. We propose to represent a data point as the sparse linear combination of its context and learn the sparse context in a supervised way to increase its discriminative ability. To this end, we proposed a novel formulation for context learning, by modeling the learning of context parameter and classifier in a unified objective, and optimizing it with 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.