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Information Fusion Based on Sparse/Collaborative Representation

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Information Fusion

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Abstract

Sparse representation follows the insight of data representation of human beings, allowing the data to be more accurately and robustly represented. Recently, many works in image classification, image retrieval, image recovery, etc., have shown its effectiveness. In contrast to the sparse representation, collaborative representation ignores the robustness but encourages algorithms to enjoy a fast computation. This chapter respectively proposes an information fusion method based on the sparse representation and two information fusion methods based on the collaborative representation. After reading this chapter, people can have preliminary knowledge on sparse/collaborative representation based fusion algorithms.

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Authors and Affiliations

  1. Harbin Institute of Technology, Shenzhen School of Computer Science and Technology Xili, Nanshan, Shenzhen, China

    Jinxing Li

  2. Department of Computer and Information Science, The University of Macau, Macau, China

    Bob Zhang

  3. School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China

    David Zhang

Authors
  1. Jinxing Li
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  2. Bob Zhang
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  3. David Zhang
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Li, J., Zhang, B., Zhang, D. (2022). Information Fusion Based on Sparse/Collaborative Representation. In: Information Fusion. Springer, Singapore. https://doi.org/10.1007/978-981-16-8976-5_2

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  • DOI: https://doi.org/10.1007/978-981-16-8976-5_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8975-8

  • Online ISBN: 978-981-16-8976-5

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