research
∙
01/06/2023
Principal Component Analysis in Space Forms
Principal component analysis (PCA) is a workhorse of modern data science...
share
research
∙
10/21/2022
Learning Ultrametric Trees for Optimal Transport Regression
Optimal transport provides a metric which quantifies the dissimilarity b...
research
∙
05/19/2022
HyperAid: Denoising in hyperbolic spaces for tree-fitting and hierarchical clustering
The problem of fitting distances by tree-metrics has received significan...
research
∙
03/07/2022
Provably Accurate and Scalable Linear Classifiers in Hyperbolic Spaces
Many high-dimensional practical data sets have hierarchical structures i...
research
∙
09/08/2021
Highly Scalable and Provably Accurate Classification in Poincare Balls
Many high-dimensional and large-volume data sets of practical relevance ...
research
∙
02/19/2021
Linear Classifiers in Mixed Constant Curvature Spaces
Embedding methods for mixed-curvature spaces are powerful techniques for...
research
∙
06/17/2020
Geometry of Comparisons
Many data analysis problems can be cast as distance geometry problems in...
research
∙
05/18/2020
Hyperbolic Distance Matrices
Hyperbolic space is a natural setting for mining and visualizing data wi...
research
∙
01/29/2019
