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About Eigenvalues from Embedding Data Complex in Low Dimension

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Advances in Swarm Intelligence (ICSI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7332))

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Abstract

LLE(Local linear embedding) and Isomap are widely used approaches for dimension reduction on data complex. The embedding results from the two methods are eigenvectors from solving specific matrices. The corresponding eigenvalues for the selected eigenvectors have important meaning for the embedding results. In this paper, the k-nn method and ε-distance approach are used for neighborhood function with parameters. Then, different datasets and parameters will be applied to obtain the embedding results and eigenvalues. The main change of eigenvalues and the corresponding embedding results will be shown in this paper.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China

    Jiun-Wei Liou & Cheng-Yuan Liou

Authors
  1. Jiun-Wei Liou
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  2. Cheng-Yuan Liou
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China

    Zhen Ji

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© 2012 Springer-Verlag Berlin Heidelberg

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Liou, JW., Liou, CY. (2012). About Eigenvalues from Embedding Data Complex in Low Dimension. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_33

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  • DOI: https://doi.org/10.1007/978-3-642-31020-1_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31019-5

  • Online ISBN: 978-3-642-31020-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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