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An Improved LMDS Algorithm

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Advances in Swarm Intelligence (ICSI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9713))

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Abstract

Classical multidimensional scaling (CMDS) is a widely used method for dimensionality reduction and data visualization, but it’s very slow. Landmark MDS (LMDS) is a fast algorithm of CMDS. In LMDS, some data points are designated as landmark points. When the intrinsic dimension of the landmark points is less than the intrinsic dimension of the data set, the embedding recovered by LMDS is not consistent with that of classical multidimensional scaling. A selection algorithm of landmark points is put forward in this paper to ensure the intrinsic dimension of the landmarks to be equal to that of the data set, so as to ensure that the embedding recovered by LMDS is the same as that of CMDS. By introducing the selection algorithm into the original LMDS, an improved LMDS algorithm called iLMDS is presented in this paper. The experimental results verify the consistency of iLMDS and CMDS.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan, China

    Taiguo Qu & Zixing Cai

Authors
  1. Taiguo Qu
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  2. Zixing Cai
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Corresponding author

Correspondence to Taiguo Qu .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzen University, Shenzhen, China

    Li Li

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© 2016 Springer International Publishing Switzerland

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Qu, T., Cai, Z. (2016). An Improved LMDS Algorithm. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-41009-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41008-1

  • Online ISBN: 978-3-319-41009-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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