arXiv:2606.14023v1 Announce Type: cross Abstract: Optimal Transport has become recently a powerful method for domain adaptation by aligning source and target distributions. We study a supervised domain adaptation problem where source and target domains are related by a rotation or a translation or a homothety in $\mathbb{R}^2$. We prove that the optimal transport map recovers the underlying map when using a $p-$norm cost with $p \geq 2$. Based on this insight, we develop a method combining $K-$means and optimal transport to estimate the underlying map, enabling adaptation of linear regression
This paper leverages recent advancements in Optimal Transport methods for domain adaptation, indicating a growing trend in applying geometrical techniques to machine learning challenges.
Improved domain adaptation techniques can significantly enhance the robustness and applicability of AI models, reducing the need for extensive retraining and increasing efficiency across various AI applications.
This research provides a more mathematically rigorous approach to solving supervised domain adaptation problems, particularly for linear regression tasks where data distributions are geometrically related.
- · AI/ML researchers
- · Companies with diverse data sources
- · Developers of transfer learning solutions
- · Industries relying on sensor data
- · N/A
The method enables more efficient and accurate adaptation of linear regression models across different but related data domains.
This foundational work could be extended to more complex model types and higher-dimensional data, broadening the scope of effective domain adaptation.
Reduced data dependency and improved model generalization could accelerate AI deployment in sectors with data scarcity or highly variable operating conditions.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG