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

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Abstract

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.

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Acknowledgments

This work was supported by the Fundamental Research Funds of Jilin University, China (Grant No. 450060491509).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Xuejie Liu & Peijuan Xu

  2. National Time Service Center, Chinese Academy of Sciences, Xi’an, 710600, China

    Jingbin Wang

  3. Graduate University of Chinese Academy of Sciences, Beijing, 100039, China

    Jingbin Wang

  4. Department of Mathematics and Computer Sciences, Eskisehir Osmangazi University, 26480, Eskisehir, Turkey

    Ming Yin

  5. Department of Computer Science, Sam Houston State University, Huntsville, TX, 77341, USA

    Benjamin Edwards

Authors
  1. Xuejie Liu
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  2. Jingbin Wang
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  3. Ming Yin
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  4. Benjamin Edwards
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  5. Peijuan Xu
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Correspondence to Ming Yin.

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Liu, X., Wang, J., Yin, M. et al. Supervised learning of sparse context reconstruction coefficients for data representation and classification. Neural Comput & Applic 28, 135–143 (2017). https://doi.org/10.1007/s00521-015-2042-5

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  • DOI: https://doi.org/10.1007/s00521-015-2042-5

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