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High Resolution Self-organizing Maps

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AI 2016: Advances in Artificial Intelligence (AI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9992))

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Abstract

Kohonen’s self organizing feature map (SOM) provides a convenient way for visualizing high dimensional input features by projecting them onto a low dimensional display space. This map has an appealing characteristic: feature vectors close to one another in the high dimensional input space remain close to one another in the low dimensional display space. Owing to the computational requirements, the display space so far remains of relatively low resolutions. In this paper, we provide an implementation of the SOM by making use of the highly parallel architecture of a graphic processing unit to increase its computational speed to allow a substantial increase in the resolution while keeping the computation to within an acceptable wall clock time. Armed with such an implementation, we find that the high resolution SOM can display intricate details concerning the relationships among the input feature vectors. These details would be lost if a low resolution SOM was deployed. The capability of the high resolution SOM is demonstrated through an application to an artificially generated dataset, the policeman dataset. The dataset allows us to design intricate relationships among the input feature vectors.

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Notes

  1. 1.

    Some of the papers do not provide sufficient detail for us to validate their claims.

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Authors and Affiliations

  1. School of Computing and Information Technology, University of Wollongong, Wollongong, Australia

    Van Tuc Nguyen, Markus Hagenbuchner & Ah Chung Tsoi

Authors
  1. Van Tuc Nguyen
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  2. Markus Hagenbuchner
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  3. Ah Chung Tsoi
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Corresponding author

Correspondence to Van Tuc Nguyen .

Editor information

Editors and Affiliations

  1. University of Tasmania, Hobart, Australia

    Byeong Ho Kang

  2. Auckland University of Technology, Auckland, New Zealand

    Quan Bai

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Nguyen, V.T., Hagenbuchner, M., Tsoi, A.C. (2016). High Resolution Self-organizing Maps. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_38

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  • DOI: https://doi.org/10.1007/978-3-319-50127-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50126-0

  • Online ISBN: 978-3-319-50127-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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