Robust adaptation regularization based on within-class scatter for domain adaptation.

Area of Science:

• Machine Learning
• Computer Vision
• Data Science

Background:

• Domain shift between training and testing data degrades model performance.
• Unsupervised domain adaptation (UDA) methods aim to mitigate this performance drop without labeled target data.
• Existing UDA methods often struggle with outlier robustness and preserving intra-class structure.

Purpose of the Study:

• Propose a novel unsupervised domain adaptation method, Robust Adaptation Regularization based on Within-Class Scatter (WCS-RAR).
• Enhance model robustness against outliers and improve classification accuracy under domain shift.
• Develop an efficient optimization strategy for the proposed method.

Main Methods:

• Employ an l2,1-norm based loss function for outlier-robust residual determination.
• Incorporate minimum within-class scatter maximization to preserve source data structure and enhance discriminative ability.
• Utilize the Representer Theorem extension with kernel trick for an elegant optimization solution.

Main Results:

• The proposed WCS-RAR method demonstrates superior performance compared to state-of-the-art methods.
• Experiments on multiple benchmark datasets validate the effectiveness of WCS-RAR in domain adaptation tasks.
• The method successfully optimizes regularized loss, within-class scatter, joint distribution, and manifold consistency.

Conclusions:

• WCS-RAR offers a robust and effective solution for unsupervised domain adaptation.
• The method's ability to handle domain shift and preserve class structure leads to improved performance.
• The efficient optimization approach makes WCS-RAR practical for real-world applications.