Research on Large-scale User Association Feature Data Visualization Based on Nonlinear Dimensionality Reduction Methods
Year: 2025
Type: conference-paper
Abstract: In large-scale user data analysis, nonlinear correlations in high-dimensional features pose challenges to data reduction and visualization. This paper constructs a dimensionality reduction framework by optimizing the t-SNE algorithm's neighborhood preservation and UMAP's manifold learning. The framework integrates adaptive bandwidth selection with hierarchical sampling for high-fidelity feature mapping, and improves visualization clarity through enhanced force-oriented layouts and multi-level scaling. Testing with million-level datasets shows the improved method achieves 92.3% feature fidelity, 3.6× computing efficiency, 59.2% reduction in edge intersections, and 0.892 cluster integrity, providing efficient solutions for large-scale user feature analysis.
Authors: Xiuzhuo Wei, Chunjie Wang, Bo Tang, Huinan Zhao
Institution: Changchun Humanities and Sciences College
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412887340