Feature Selection for Unsupervised Domain Adaptation using Optimal Transport
This work addresses feature selection for domain adaptation, which is an incremental improvement for tasks like computer-aided diagnosis in clinical imaging.
The paper tackles the problem of feature selection in unsupervised domain adaptation by proposing a method based on optimal transport theory, which sorts features by similarity across domains to improve classification performance and computational efficiency, validated on benchmark and clinical imaging datasets with promising results.
In this paper, we propose a new feature selection method for unsupervised domain adaptation based on the emerging optimal transportation theory. We build upon a recent theoretical analysis of optimal transport in domain adaptation and show that it can directly suggest a feature selection procedure leveraging the shift between the domains. Based on this, we propose a novel algorithm that aims to sort features by their similarity across the source and target domains, where the order is obtained by analyzing the coupling matrix representing the solution of the proposed optimal transportation problem. We evaluate our method on a well-known benchmark data set and illustrate its capability of selecting correlated features leading to better classification performances. Furthermore, we show that the proposed algorithm can be used as a pre-processing step for existing domain adaptation techniques ensuring an important speed-up in terms of the computational time while maintaining comparable results. Finally, we validate our algorithm on clinical imaging databases for computer-aided diagnosis task with promising results.