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Multiscale Slant Discriminant Analysis for Classification

  • Conference paper
Modern Advances in Applied Intelligence (IEA/AIE 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8481))

  • 2014 Accesses

Abstract

In this paper, a novel feature extraction algorithm in Curvelet domain is proposed. Slant Discriminant Analysis (SDA) is a powerful matrix-based approach for feature extraction. The proposed algorithm aims to extract the most discriminant features of the samples in Curvelet Domain. Compared with several classical algorithms, the efficiency of the proposed algorithm is confirmed by the experimental results.

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Author information

Authors and Affiliations

  1. Heihe University, Heihe, Heilongjiang, China

    QingLiang Jiao

Authors
  1. QingLiang Jiao
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University, TX 78666, San Marcos, USA

    Moonis Ali

  2. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

  4. Department of Electronics Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan (ROC)

    Mong-Fong Horng

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© 2014 Springer International Publishing Switzerland

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Jiao, Q. (2014). Multiscale Slant Discriminant Analysis for Classification. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_29

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  • DOI: https://doi.org/10.1007/978-3-319-07455-9_29

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07454-2

  • Online ISBN: 978-3-319-07455-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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