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  Data Visualization using Linear and Non-linear Dimensionality Reduction Methods

Kim, J., & Youn, J.-S. (2018). Data Visualization using Linear and Non-linear Dimensionality Reduction Methods. Journal of the Korea Society of Computer and Information, 23(12): 177, pp. 21-26. doi:10.9708/jksci.2018.23.12.021.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-C701-C Version Permalink: http://hdl.handle.net/21.11116/0000-0002-C704-9
Genre: Journal Article

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Kim, J1, 2, Author              
Youn, J-S, Author
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1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: As the large amount of data can be efficiently stored, the methods extracting meaningful features from big data has become important. Especially, the techniques of converting high- to low-dimensional data are crucial for the ’Data visualization’. In this study, principal component analysis (PCA; linear dimensionality reduction technique) and Isomap (non-linear dimensionality reduction technique) are introduced and applied to neural big data obtained by the functional magnetic resonance imaging (fMRI). First, we investigate how much the physical properties of stimuli are maintained after the dimensionality reduction processes. We moreover compared the amount of residual variance to quantitatively compare the amount of information that was not explained. As result, the dimensionality reduction using Isomap contains more information than the principal component analysis. Our results demonstrate that it is necessary to consider not only linear but also nonlinear characteristics in the big data analysis.

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 Dates: 2018-12
 Publication Status: Published in print
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 Identifiers: DOI: 10.9708/jksci.2018.23.12.021
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Title: Journal of the Korea Society of Computer and Information
Source Genre: Journal
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Pages: - Volume / Issue: 23 (12) Sequence Number: 177 Start / End Page: 21 - 26 Identifier: ISSN: 1598-849X