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Visualization Method of Viewpoints Latent in a Dataset

  • Hideaki Ishibashi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11303)

Abstract

The purpose of this study is to propose a paradigm visualizing the viewpoints from datasets observed by multiple viewpoints, which is referred to as Latent Viewpoint Visualization (LVV). Since LVV visualizes similarity/dissimilarity among the viewpoints, it has many applications such as the authors’ perspective from news articles and the psychological measurements’ aspect from psychological surveys. In this study, we propose the concept of LVV and develop a preliminary algorithm. Furthermore, we experimentally show what kind of information can be visualized by LVV using several datasets.

Keywords

Latent viewpoint visualization Multi-view learning Data integration Grassmann manifold 

Notes

Acknowledgement

The author is grateful to Prof. Tetsuo Furukawa from the Kyushu Institute of Technology for advising about many topics treated in this paper. The author thanks Assoc. Prof. Hideitsu Hino from the Institute of Statistical Mathematics for his valuable comments. A part of this work was supported by KAKENHI (17H01793) and JST CREST (JPMJCR1761). This work was partially supported by Dr. Takashi Ohkubo.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Life Science and Systems EngineeringKyushu Institute of TechnologyKitakyushuJapan
  2. 2.Research Center for Statistical Machine LearningThe Institute of Statistical MathematicsTachikawaJapan

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