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RETRACTED CHAPTER: Non-peaked Discriminant Analysis for Image Representation

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Image and Video Technology (PSIVT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11854))

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Abstract

The L1-norm has been used as the distance metric in robust discriminant analysis. However, it is not sufficiently robust and thus we propose the use of cutting L1-norm. Since this norm is helpful for eliminating outliers in learning models, the proposed non-peaked discriminant analysis is better able to perform feature extraction tasks for image classification. We also show that cutting L1-norm can be equivalently computed using the difference of two special convex functions and present an efficient iterative algorithm for the optimization of proposed objective. The theoretical insights and effectiveness of the proposed algorithm are verified by experimental results on images from three datasets.

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Change history

  • 11 November 2019

    The Editors have retracted this chapter [1] because it presents data without authorization for use and contains significant overlap with [2]. Both authors agree to this retraction but not to the wording of the retraction notice.

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Author information

Authors and Affiliations

  1. Nanjing Forestry University, Nanjing, China

    Xijian Fan & Qiaolin Ye

Authors
  1. Xijian Fan
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  2. Qiaolin Ye
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Corresponding author

Correspondence to Xijian Fan .

Editor information

Editors and Affiliations

  1. Chonnam National University, Gwangju, Korea (Republic of)

    Chilwoo Lee

  2. Dalian University of Technology, Dalian, China

    Zhixun Su

  3. National Institute of Informatics, Tokyo, Japan

    Akihiro Sugimoto

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Fan, X., Ye, Q. (2019). RETRACTED CHAPTER: Non-peaked Discriminant Analysis for Image Representation. In: Lee, C., Su, Z., Sugimoto, A. (eds) Image and Video Technology. PSIVT 2019. Lecture Notes in Computer Science(), vol 11854. Springer, Cham. https://doi.org/10.1007/978-3-030-34879-3_24

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  • DOI: https://doi.org/10.1007/978-3-030-34879-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34878-6

  • Online ISBN: 978-3-030-34879-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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