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@article{ART002419074},
author={Junsuk Kim and Youn Joo Sang},
title={Data Visualization using Linear and Non-linear Dimensionality Reduction Methods},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2018},
volume={23},
number={12},
pages={21-26},
doi={10.9708/jksci.2018.23.12.021}

TY - JOUR
AU - Junsuk Kim
AU - Youn Joo Sang
TI - Data Visualization using Linear and Non-linear Dimensionality Reduction Methods
JO - Journal of The Korea Society of Computer and Information
PY - 2018
VL - 23
IS - 12
PB - The Korean Society Of Computer And Information
SP - 21
EP - 26
SN - 1598-849X
AB - 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.
KW - Dimensionality reduction;Principal component analysis;Isomap;Residual variance
DO - 10.9708/jksci.2018.23.12.021
ER -

Junsuk Kim and Youn Joo Sang. (2018). Data Visualization using Linear and Non-linear Dimensionality Reduction Methods. Journal of The Korea Society of Computer and Information, 23(12), 21-26.

Junsuk Kim and Youn Joo Sang. 2018, "Data Visualization using Linear and Non-linear Dimensionality Reduction Methods", Journal of The Korea Society of Computer and Information, vol.23, no.12 pp.21-26. Available from: doi:10.9708/jksci.2018.23.12.021

Junsuk Kim, Youn Joo Sang "Data Visualization using Linear and Non-linear Dimensionality Reduction Methods" Journal of The Korea Society of Computer and Information 23.12 pp.21-26 (2018) : 21.

Junsuk Kim, Youn Joo Sang. Data Visualization using Linear and Non-linear Dimensionality Reduction Methods. 2018; 23(12), 21-26. Available from: doi:10.9708/jksci.2018.23.12.021

Junsuk Kim and Youn Joo Sang. "Data Visualization using Linear and Non-linear Dimensionality Reduction Methods" Journal of The Korea Society of Computer and Information 23, no.12 (2018) : 21-26.doi: 10.9708/jksci.2018.23.12.021

Junsuk Kim; Youn Joo Sang. Data Visualization using Linear and Non-linear Dimensionality Reduction Methods. Journal of The Korea Society of Computer and Information, 23(12), 21-26. doi: 10.9708/jksci.2018.23.12.021

Junsuk Kim; Youn Joo Sang. Data Visualization using Linear and Non-linear Dimensionality Reduction Methods. Journal of The Korea Society of Computer and Information. 2018; 23(12) 21-26. doi: 10.9708/jksci.2018.23.12.021

Junsuk Kim, Youn Joo Sang. Data Visualization using Linear and Non-linear Dimensionality Reduction Methods. 2018; 23(12), 21-26. Available from: doi:10.9708/jksci.2018.23.12.021

Junsuk Kim and Youn Joo Sang. "Data Visualization using Linear and Non-linear Dimensionality Reduction Methods" Journal of The Korea Society of Computer and Information 23, no.12 (2018) : 21-26.doi: 10.9708/jksci.2018.23.12.021