1 citations · 2 across the 5 of their papers we have counts for
5 papers
A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Data
Mary Chriselda Antony Oliver, Kaitlyn Hohmeier, Tuyen Tran +3
High-throughput single-cell and spatial transcriptomic technologies provide high-resolution snapshots of heterogeneous cellular states, but their destructive nature prevents repeat…
Wasserstein Mahalanobis Distances for Recovering Latent Geometry
Chuxiangbo Wang, Shiying Li, Caroline Moosmüller
The Mahalanobis distance is a fundamental covariance-adapted metric for multivariate data and plays a central role in recovering latent geometry from nonlinear observations. We ext…
Manifold learning in Wasserstein space
Keaton Hamm, Caroline Moosmüller, Bernhard Schmitzer +1
This paper aims at building the theoretical foundations for manifold learning algorithms in the space of absolutely continuous probability measures …
Linearized Wasserstein dimensionality reduction with approximation guarantees
Alexander Cloninger, Keaton Hamm, Varun Khurana +1
We introduce LOT Wassmap, a computationally feasible algorithm to uncover low-dimensional structures in the Wasserstein space. The algorithm is motivated by the observation that ma…
Level-dependent interpolatory Hermite subdivision schemes and wavelets
Mariantonia Cotronei, Caroline Moosmüller, Tomas Sauer +1
We study many properties of level-dependent Hermite subdivision, focusing on schemes preserving polynomial and exponential data. We specifically consider interpolatory schemes, whi…