Global and local structure preserving GPU t-SNE methods for large-scale applications
作者：
Highlights：
• Fast SWW-AtSNE method for dimensionality reduction preserves Global/Local structures.
• The introduction of a new metric to quantify the global structure preservation.
• Analysis of GPU t-SNE based methods in real-world applications with large datasets.
• PCA initialization in SWW-tSNE is fundamental to preserve global structures.
• UMAP and AtSNE does not preserve global structures better than SWW-tSNE.
摘要
•Fast SWW-AtSNE method for dimensionality reduction preserves Global/Local structures.•The introduction of a new metric to quantify the global structure preservation.•Analysis of GPU t-SNE based methods in real-world applications with large datasets.•PCA initialization in SWW-tSNE is fundamental to preserve global structures.•UMAP and AtSNE does not preserve global structures better than SWW-tSNE.
论文关键词：t-SNE,Large-scale data,GPU,Dimensionality reduction
论文评审过程：Received 3 February 2021, Revised 20 December 2021, Accepted 13 March 2022, Available online 23 March 2022, Version of Record 18 April 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116918
