@inproceedings{fbff163956f84aa48b66d20578acb995,
title = "HYPERBOLIC DIFFUSION PROCRUSTES ANALYSIS FOR INTRINSIC REPRESENTATION OF HIERARCHICAL DATA SETS",
abstract = "In this paper, we present Hyperbolic Diffusion Procrustes Analysis (HDPA), a new method for informative representation of hierarchical datasets based on hyperbolic geometry, diffusion geometry, and Procrustes analysis. Our method jointly embeds multiple datasets in a product manifold of hyperbolic spaces, where the data's hidden common hierarchical structure is provably recovered. In addition, our method generates an intrinsic embedding that accommodates the joint representation of multiple datasets with different features, acquired by different equipment, at different sites, or under different environmental conditions. Experimental results demonstrate the efficacy of HDPA on three biomedical datasets comprising heterogeneous gene expression and mass cytometry data.",
keywords = "Domain adaptation, Graph diffusion, Hyperbolic geometry, Manifold learning, Procrustes analysis",
author = "Lin, {Ya Wei Eileen} and Yuval Kluger and Ronen Talmon",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
year = "2024",
doi = "https://doi.org/10.1109/ICASSP48485.2024.10446370",
language = "الإنجليزيّة",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "6325--6329",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings",
}