A Survey on Dimension Reduction Algorithms in Big Data Visualization

Zheng Sun; Weiqing Xing; Wenjun Guo; Seungwook Kim; Hongze Li; Wenye Li; Jianru Wu; Yiwen Zhang; Bin Cheng; Shenghui Cheng

Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. 9th EAI International Conference, CloudComp 2019, and 4th EAI International Conference, SmartGIFT 2019, Beijing, China, December 4-5, 2019, and December 21-22, 2019

Research Article

A Survey on Dimension Reduction Algorithms in Big Data Visualization

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@INPROCEEDINGS{10.1007/978-3-030-48513-9_31,
    author={Zheng Sun and Weiqing Xing and Wenjun Guo and Seungwook Kim and Hongze Li and Wenye Li and Jianru Wu and Yiwen Zhang and Bin Cheng and Shenghui Cheng},
    title={A Survey on Dimension Reduction Algorithms in Big Data Visualization},
    proceedings={Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. 9th EAI International Conference, CloudComp 2019, and 4th EAI International Conference, SmartGIFT 2019, Beijing, China, December 4-5, 2019, and December 21-22, 2019},
    proceedings_a={CLOUDCOMP},
    year={2020},
    month={6},
    keywords={High dimension Dimension reduction Radar map Data visualization},
    doi={10.1007/978-3-030-48513-9_31}
}

Zheng Sun
Weiqing Xing
Wenjun Guo
Seungwook Kim
Hongze Li
Wenye Li
Jianru Wu
Yiwen Zhang
Bin Cheng
Shenghui Cheng
Year: 2020
A Survey on Dimension Reduction Algorithms in Big Data Visualization
CLOUDCOMP
Springer
DOI: 10.1007/978-3-030-48513-9_31

Zheng Sun¹, Weiqing Xing², Wenjun Guo¹, Seungwook Kim¹, Hongze Li¹, Wenye Li¹, Jianru Wu², Yiwen Zhang³, Bin Cheng², Shenghui Cheng¹^,*

1: Shenzhen Research Institute of Big Data and the Chinese University of Hong Kong
2: Shenzhen Institute of Pharmacovigilance and Risk Management
3: Anhui University

*Contact email: chengshenghui@cuhk.edu.cn

Abstract

In practical applications, the data set we deal with is typically high dimensional, which not only affects training speed but also makes it difficult for people to analyze and understand. It is known as “the curse of dimensionality”. Therefore, dimensionality reduction plays a key role in the multidimensional data analysis. It can improve the performance of the model and assist people in understanding the structure of data. These methods are widely used in financial field, medical field e.g. adverse drug reactions and so on. In this paper, we present a number of dimension reduction algorithms and compare their strengths and shortcomings. For more details about these algorithms, please visit our Dagoo platform via .

Keywords: High dimension Dimension reduction Radar map Data visualization

Published: 2020-06-05
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-030-48513-9_31