Research on data dimensionality reduction visualization method based on principal component analysis

Ming Li; Longyue Li; Wanwan Cao; Xiaoyu Yin

doi:10.1117/12.3004267

25 September 2023 Research on data dimensionality reduction visualization method based on principal component analysis

Ming Li, Longyue Li, Wanwan Cao, Xiaoyu Yin

Proceedings Volume 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023); 127881I (2023) https://doi.org/10.1117/12.3004267
Event: Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Recently, data in enterprises is showing an explosive trend, and many companies have begun to build or use data centers for unified management and mining of data. However, the visualization and analysis of data in China and Taiwan has become a common problem in the industry. To deal with massive high-dimensional data, traditional visualization methods are difficult to find the potential connection of data. Therefore, this paper proposes a data dimensionality reduction visualization method based on principal component analysis, and the method has a good application prospect in the big data processing of energy and electricity. The experimental results demonstrate that the suggested method can effectively integrate the data features and visually analyze the data.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ming Li, Longyue Li, Wanwan Cao, and Xiaoyu Yin "Research on data dimensionality reduction visualization method based on principal component analysis", Proc. SPIE 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 127881I (25 September 2023); https://doi.org/10.1117/12.3004267

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