Polygonal Coordinate System: Visualizing high-dimensional data using geometric DR, and a deterministic version of t-SNE
作者：
Highlights：
• A global geometric approach for embedding-based dimensionality reduction in 2D/3D.
• An effcient method across a regular polygon named Polygonal Coordinate System (PCS).
• A deterministic version of t-SNE is proposed to explore the strengths of PCS + t-SNE.
• PCS can properly handle large amounts of data without in-memory loading limitations.
• Comparative studies show that PCS outperforms previous techniques in many aspects.
摘要
•A global geometric approach for embedding-based dimensionality reduction in 2D/3D.•An effcient method across a regular polygon named Polygonal Coordinate System (PCS).•A deterministic version of t-SNE is proposed to explore the strengths of PCS + t-SNE.•PCS can properly handle large amounts of data without in-memory loading limitations.•Comparative studies show that PCS outperforms previous techniques in many aspects.
论文关键词：Dimensionality reduction,Embedding,Visualization,Machine learning,Big data
论文评审过程：Received 28 January 2020, Revised 8 December 2020, Accepted 14 February 2021, Available online 26 February 2021, Version of Record 31 March 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114741
