Linear optimal partial transport embedding
Yikun Bai, Ivan Vladimir Medri, Rocio Diaz Martin, Rana Shahroz, Soheil Kolouri
Abstract
Optimal transport (OT) has gained popularity due to its various applications in fields such as machine learning, statistics, and signal processing. However, the balanced mass requirement limits its performance in practical problems. To address these limitations, variants of the OT problem, including unbalanced OT, Optimal partial transport (OPT), and Hellinger Kantorovich (HK), have been proposed. In this paper, we propose the Linear optimal partial transport (LOPT) embedding, which extends the (local) linearization technique on OT and HK to the OPT problem. The proposed embedding allows for faster computation of OPT distance between pairs of positive measures. Besides our theoretical contributions, we demonstrate the LOPT embedding technique in point-cloud interpolation and PCA analysis. Our code is available at https://github.com/Baio0/LinearOPT.
BibTeX
@inproceedings{icml2023_linearoptimalpar,
title = {Linear optimal partial transport embedding},
author = {Yikun Bai and Ivan Vladimir Medri and Rocio Diaz Martin and Rana Shahroz and Soheil Kolouri},
booktitle = {ICML 2023},
year = {2023}
}