Marginal Subspace Learning With Group Low-Rank for Unsupervised Domain Adaptation

Area of Science:

• Machine Learning
• Computer Vision
• Artificial Intelligence

Background:

• Unsupervised domain adaptation aims to build models for unlabeled target data using labeled source data with different distributions.
• Existing methods focus on domain-invariant features but often neglect group discriminability, hindering recognition performance.
• Domain shift presents a significant challenge in applying models across different datasets or environments.

Purpose of the Study:

• To propose a novel unsupervised domain adaptation method, marginal subspace learning with group low-rank (MSL-GLR).
• To extract feature representations that are both domain-invariant and discriminative.
• To improve the recognition accuracy of models in target domains with limited or no labeled data.

Main Methods:

• MSL-GLR employs a retargeting strategy to enforce margin maximization for correct classification.
• A class-induced low-rank constraint is imposed to group samples of the same class within their respective subspaces.
• An efficient algorithm using the alternating direction method of multipliers (ADMM) is developed to solve the optimization problem.

Main Results:

• The proposed MSL-GLR method effectively extracts domain-invariant and discriminative feature representations.
• The class-induced low-rank constraint significantly improves the discriminant ability of the projected features.
• Experimental evaluations on benchmark datasets demonstrate the superior effectiveness of MSL-GLR compared to existing methods.

Conclusions:

• MSL-GLR offers a promising approach to address the limitations of current unsupervised domain adaptation techniques.
• By simultaneously considering domain invariance and group discriminability, MSL-GLR enhances model reliability on target domains.
• The method provides a robust framework for feature learning in scenarios with domain shift.