Visualization of large-scale user-related feature data based on nonlinear dimensionality reduction method

DOI

10.2991/978-94-6463-419-8_7How to use a DOI?

Keywords

nonlinear dimensionality; user-related feature data; Visualization; t-SNE

Abstract

Powerful tools and technical support are needed to extract meaningful information from massive data, understand the relationship between users and discover potential patterns. In this context, the visualization of large-scale user-related feature data has emerged as an important means to deal with the flood of data. The research direction of this paper is the visualization platform of large-scale user-related feature data based on nonlinear dimensionality reduction method, and the overall architecture of the visualization platform of large-scale user-related feature data is designed. Inspired by t-SNE algorithm, a new dimensionality reduction visualization method, namely Laplacian random neighbor distribution based on graph regularization, is proposed. This method aims to project large-scale user-related feature data into the visualization space, so that the visualization results can not only maintain the local neighbor structure of the original high-dimensional spatial data, but also maintain the overall structure of the data, and make the distribution of sample points in 2D space relatively loose. The research results show that the processing time of the test process is gradually reduced, which reflects the acceleration ratio of parallel processing. The improved t-SNE nonlinear dimensionality reduction model well preserves the category relationship between large-scale user-related feature data, and the data after dimensionality reduction is obvious.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiuzhuo Wei
AU  - Chunjie Wang
AU  - Bo Tang
AU  - Huinan Zhao
PY  - 2024
DA  - 2024/05/07
TI  - Visualization of large-scale user-related feature data based on nonlinear dimensionality reduction method
BT  - Proceedings of the 3rd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2024)
PB  - Atlantis Press
SP  - 44
EP  - 52
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-419-8_7
DO  - 10.2991/978-94-6463-419-8_7
ID  - Wei2024
ER  -