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Least Absolute Deviation Cut

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Rough Sets and Knowledge Technology (RSKT 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6954))

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Abstract

The paper discussed a new additive extension of minimum cut by simultaneously minimizing intra cluster similarity bias and inter cluster similarity, Least Absolute Deviation Cut (LAD cut). The LAD cut can be proved convergent in finite iterative steps, and its theoretical conditions that the LAD cut can work well is also presented. Furthermore, its computational complexity is also analyzed. Numerical experimental results show that LAD cut may be useful for image segmentation.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Beijing Jiaotong University, Beijing, 100044, P.R. China

    Jian Yu & Liping Jing

Authors
  1. Jian Yu
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  2. Liping Jing
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, S4S 0A2, Saskatchewan, Canada

    JingTao Yao

  2. Department of Applied Computer Science, University of Winnipeg, R3B 2E9, Winnipeg, Canada

    Sheela Ramanna

  3. Institute of Computer Science & Technology, Chongqing University of Posts and Telecommunications, 400065, Chongqing, P.R. China

    Guoyin Wang

  4. Chair of Computer Science, University of Rzeszów, Poland

    Zbigniew Suraj

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© 2011 Springer-Verlag Berlin Heidelberg

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Yu, J., Jing, L. (2011). Least Absolute Deviation Cut. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_93

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  • DOI: https://doi.org/10.1007/978-3-642-24425-4_93

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24424-7

  • Online ISBN: 978-3-642-24425-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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