Feature Selection for Unsupervised Domain Adaptation using Optimal Transport

TL;DR

This paper introduces a novel feature selection method for unsupervised domain adaptation using optimal transport theory, improving classification accuracy and computational efficiency across various datasets.

Contribution

The paper presents a new feature selection algorithm based on optimal transport that leverages domain shift analysis to enhance unsupervised domain adaptation.

Findings

Improves classification performance on benchmark datasets.

Speeds up existing domain adaptation methods.

Effective in clinical imaging diagnosis tasks.

Abstract

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.