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Recent Advances in Intelligent Engineering pp 65–80Cite as

A Priori Membership for Data Representation: Case Study of SPECT Heart Data Set

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Part of the book series: Topics in Intelligent Engineering and Informatics ((TIEI,volume 14))

Abstract

The image is important source for analytics. However, global reductions and local features are hard to solve. This paper proposes an innovative membership for data representation thus providing an easier and simpler way for both. Technically, it adopts relevant preference for inference. In this paper, its operations include reducing variables and identifying sparse features by taking advantage of evidence. In illustration, an image study of proton emission in UCI SPECT is presented. It discloses key variables of abnormal samples buried in normal range. The contribution of this paper lies in providing priori data to enhance representation learning.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam

    Hamido Fujita

  2. Software and Information Science, Iwate Prefectural University, Takizawa, 020-0693, Japan

    Hamido Fujita

  3. Department of Information Management, Chung Hua University, 30012, Hsinchu, Taiwan

    Yu-Chien Ko

Authors
  1. Hamido Fujita
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  2. Yu-Chien Ko
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Corresponding author

Correspondence to Hamido Fujita .

Editor information

Editors and Affiliations

  1. Obuda University, Budapest, Hungary

    Levente Kovács

  2. Óbuda University, Budapest, Hungary

    Tamás Haidegger

  3. Óbuda University, Budapest, Hungary

    Anikó Szakál

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Fujita, H., Ko, YC. (2020). A Priori Membership for Data Representation: Case Study of SPECT Heart Data Set. In: Kovács, L., Haidegger, T., Szakál, A. (eds) Recent Advances in Intelligent Engineering. Topics in Intelligent Engineering and Informatics, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-14350-3_4

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