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A Distance Adaptive Embedding Method in Dimension Reduction

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Trustworthy Computing and Services (ISCTCS 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 320))

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Abstract

The distribution preservation is a challenge inthe dimension reduction methods. This paper proposes a distance adaptive embedding method (DAE). The DAE method includes the cosine similarity technology and a new distance transformation function. It has the characteristics of easy handling and strong similarity distinction. The DAE method can make small loss value and good cluster discrimination by using the new distance transformation function in the embedding.The experiment results show that the DAE method has a good performance in distribution preservation, better than the performance of the multidimensional scaling method.

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Author information

Authors and Affiliations

  1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China

    Yanting Niu, Yueming Lu & Songlin Sun

  2. Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing, China

    Yanting Niu, Yueming Lu & Songlin Sun

  3. School of Humanities, Beijing University of Posts and Telecommunications, Beijing, China

    Fangwei Zhang

Authors
  1. Yanting Niu
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  2. Yueming Lu
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  3. Fangwei Zhang
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  4. Songlin Sun
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Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Yuyu Yuan  & Xu Wu  & 

  2. The School of Telecommunications Engineering, Beijing University of Posts and Telecommunications Beijing, P. O. Box 128, 100876, Beijing, China

    Yueming Lu

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Niu, Y., Lu, Y., Zhang, F., Sun, S. (2013). A Distance Adaptive Embedding Method in Dimension Reduction. In: Yuan, Y., Wu, X., Lu, Y. (eds) Trustworthy Computing and Services. ISCTCS 2012. Communications in Computer and Information Science, vol 320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35795-4_34

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  • DOI: https://doi.org/10.1007/978-3-642-35795-4_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35794-7

  • Online ISBN: 978-3-642-35795-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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