Theoretical Foundations of t-SNE for Visualizing High-Dimensional
Clustered Data

Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data

16 May 2021

T. Tony Cai

Papers citing "Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data"

11 / 11 papers shown

Title
Learning Joint ID-Textual Representation for ID-Preserving Image Synthesis Zichuan Liu Liming Jiang Qing Yan Yumin Jia Hao Kang Xin Lu DiffM 31 0 0 19 Apr 2025
The Shape of Attraction in UMAP: Exploring the Embedding Forces in Dimensionality Reduction Mohammad Tariqul Islam Jason W. Fleischer 149 0 0 12 Mar 2025
Strada-LLM: Graph LLM for traffic prediction Seyed Mohamad Moghadas Yangxintong Lyu Bruno Cornelis Alexandre Alahi Adrian Munteanu AI4TS 56 0 0 17 Feb 2025
Unified dimensionality reduction techniques in chronic liver disease detection Anand Karna Naina Khan Rahul Rauniyar Prashant Giridhar Shambharkar OOD 36 0 0 31 Dec 2024
Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective Zhexuan Liu Rong Ma Yiqiao Zhong 60 0 0 22 Oct 2024
Online t-SNE for single-cell RNA-seq Hui Ma Kai Chen OnRL 24 0 0 21 Jun 2024
Sailing in high-dimensional spaces: Low-dimensional embeddings through angle preservation Jonas Fischer Rong Ma 35 0 0 14 Jun 2024
CWD30: A Comprehensive and Holistic Dataset for Crop Weed Recognition in Precision Agriculture Talha Ilyas D. M. S. Arsa Khubaib Ahmad Yongchae Jeong Okjae Won Jong Hoon Lee H. Kim 8 3 0 17 May 2023
A Spectral Method for Assessing and Combining Multiple Data Visualizations Rong Ma Eric D. Sun James Y. Zou 11 11 0 25 Oct 2022
Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach Xiucai Ding Rongkai Ma 23 9 0 28 Feb 2022
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization Yingfan Wang Haiyang Huang Cynthia Rudin Yaron Shaposhnik 159 301 0 08 Dec 2020