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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 Workshops pp 16–31Cite as

Approximation of N-Way Principal Component Analysis for Organ Data

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10118))

Abstract

We apply multilinear principal component analysis to dimension reduction and classification of human volumetric organ data, which are expressed as multiway array data. For the decomposition of multiway array data, tensor-based principal component analysis extracts multilinear structure of the data. We numerically clarify that low-pass filtering after the multidimensional discrete cosine transform efficiently approximates data dimension reduction procedure based on the tensor principal component analysis.

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Author information

Authors and Affiliations

  1. School of Advanced Integration Science, Chiba University, Chiba, Japan

    Hayato Itoh

  2. Institute of Management and Information Technologies, Chiba University, Chiba, Japan

    Atsushi Imiya

  3. Graduate School of Engineering, Nagasaki University, Nagasaki, Japan

    Tomoya Sakai

Authors
  1. Hayato Itoh
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  2. Atsushi Imiya
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  3. Tomoya Sakai
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Corresponding author

Correspondence to Hayato Itoh .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Itoh, H., Imiya, A., Sakai, T. (2017). Approximation of N-Way Principal Component Analysis for Organ Data. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-54526-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54525-7

  • Online ISBN: 978-3-319-54526-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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