Supervised Optimal Transport
数学专题报告
报告题目(Title)：Supervised Optimal Transport
报告人(Speaker)：Prof. Yanxiang Zhao（George Washington University）
地点(Place)：ZOOM会议:ID: 828 695 49163, Password: 626117
时间(Time)：4月20日 （星期三）10:00am-11:00am
邀请人(Inviter)：蔡勇勇
报告摘要
Optimal Transport, a theory for optimal allocation of resources, is widely used in various fields such as astrophysics, machine learning, and imaging science. However, many applications impose elementwise constraints on the transport plan which traditional optimal transport cannot enforce. Here we introduce Supervised Optimal Transport (sOT) that formulates a constrained optimal transport problem where couplings between certain elements are prohibited according to specific applications. sOT is proved to be equivalent to an $l^1$ penalized optimization problem, from which efficient algorithms are designed to solve its entropy regularized formulation. We demonstrate the capability of sOT by comparing it to other variants and extensions of traditional OT in color transfer problem. We also study the barycenter problem in sOT formulation, where we discover and prove a unique reverse and portion selection (control) mechanism. Supervised optimal transport is broadly applicable to applications in which constrained transport plan is involved and the original unit should be preserved by avoiding normalization.
