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A New Sparse Representation Algorithm for Semi-supervised Signal Classification

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Artificial Intelligence and Signal Processing (AISP 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 427))

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Abstract

The performance of many Sparse Representation (SR) based signal classification tasks is highly dependent on the availability of the datasets with a large amount of labeled data points. However, in many cases, accessing to sufficient labeled data may be expensive or time consuming, whereas acquiring a large amount of unlabeled data is relatively easy. In this paper, we propose a new SR based classification method which utilizes the information of the unlabeled data as well as the labeled data. Experimental results show that the proposed method outperforms the state of the art SR based classification methods.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, University of Kashan, Kashan, Iran

    Azam Andalib & Seyed Morteza Babamir

Authors
  1. Azam Andalib
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  2. Seyed Morteza Babamir
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Corresponding author

Correspondence to Azam Andalib .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Ali Movaghar

  2. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Mansour Jamzad

  3. Sharif University of Technology, Tehran, Iran

    Hossein Asadi

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Andalib, A., Babamir, S.M. (2014). A New Sparse Representation Algorithm for Semi-supervised Signal Classification. In: Movaghar, A., Jamzad, M., Asadi, H. (eds) Artificial Intelligence and Signal Processing. AISP 2013. Communications in Computer and Information Science, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-10849-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-10849-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10848-3

  • Online ISBN: 978-3-319-10849-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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