1 citations · 1 across the 1 of their papers we have counts for
6 papers
Inference for Similarity and Alignability between Noisy High-Dimensional Datasets
Hongrui Chen, Rong Ma
The rapid growth of high-dimensional datasets across a wide range of scientific domains has created an urgent need for new statistical methods to compare distributions with underly…
Uncovering smooth structures in single-cell data with PCS-guided neighbor embeddings
Rong Ma, Xi Li, Jingyuan Hu +1
Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions. Many biological processes unfold along continuous trajectories, yet…
Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective
Zhexuan Liu, Rong Ma, Yiqiao Zhong
Visualizing high-dimensional data is essential for understanding biomedical data and deep learning models. Neighbor embedding methods, such as t-SNE and UMAP, are widely used but c…
Entropic Optimal Transport Eigenmaps for Nonlinear Alignment and Joint Embedding of High-Dimensional Datasets
Boris Landa, Yuval Kluger, Rong Ma
Embedding high-dimensional data into a low-dimensional space is an indispensable component of data analysis. In numerous applications, it is necessary to align and jointly embed mu…
Sailing in high-dimensional spaces: Low-dimensional embeddings through angle preservation
Jonas Fischer, Rong Ma
Low-dimensional embeddings (LDEs) of high-dimensional data are ubiquitous in science and engineering. They allow us to quickly understand the main properties of the data, identify…
Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators
Xiucai Ding, Rong Ma
Integrative analysis of multiple heterogeneous datasets has become standard practice in many research fields, especially in single-cell genomics and medical informatics. Existing a…