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Representing Data by Sparse Combination of Contextual Data Points for Classification

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Advances in Neural Networks – ISNN 2015 (ISNN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9377))

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Abstract

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.

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Author information

Authors and Affiliations

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

    Jingyan Wang

  2. Graduate University of Chinese Academy of Sciences, Beijing, 100049, China

    Jingyan Wang

  3. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, China

    Jingyan Wang

  4. Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, 18015, USA

    Yihua Zhou

  5. Electronic Engineering College, Naval University of Engineering, Wuhan, 430033, China

    Ming Yin & Shaochang Chen

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

    Benjamin Edwards

Authors
  1. Jingyan Wang
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  2. Yihua Zhou
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  3. Ming Yin
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  4. Shaochang Chen
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  5. Benjamin Edwards
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xiaolin Hu

  2. Fuzhou University, Fuzhou, China

    Yousheng Xia

  3. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China

    Yunong Zhang

  4. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Dongbin Zhao

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Wang, J., Zhou, Y., Yin, M., Chen, S., Edwards, B. (2015). Representing Data by Sparse Combination of Contextual Data Points for Classification. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_41

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  • DOI: https://doi.org/10.1007/978-3-319-25393-0_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25392-3

  • Online ISBN: 978-3-319-25393-0

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