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

  • Hamido FujitaEmail author
  • Yu-Chien Ko
Chapter
Part of the Topics in Intelligent Engineering and Informatics book series (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.

Keywords

Analytics Image Data representation Priori data Evidence 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Information TechnologyHo Chi Minh City University of Technology (HUTECH)Ho Chi Minh CityVietnam
  2. 2.Software and Information ScienceIwate Prefectural UniversityTakizawaJapan
  3. 3.Department of Information ManagementChung Hua UniversityHsinchuTaiwan

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