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Data Visualization using Linear and Non-linear Dimensionality Reduction Methods

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Title
Data Visualization using Linear and Non-linear Dimensionality Reduction Methods
Author(s)
Junsuk Kim; Joosang Youn
Publication Date
2018-12
Journal
Journal of The Korea Society of Computer and Information 한국컴퓨터정보학회논문지, v.23, no.12, pp.21 - 26
Publisher
The Korean Society Of Computer And Information한국컴퓨터정보학회
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.
URI
https://pr.ibs.re.kr/handle/8788114/5373
DOI
10.9708/jksci.2018.23.12.000
ISSN
1598-849X
Appears in Collections:
HiddenCommunity > 1. Journal Papers (저널논문)
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59-1_김준석 Data Visualization using Linear and Non-linear Dimensionality Reduction Methods.pdfDownload

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