Subspace Distribution Adaptation Frameworks for Domain Adaptation.

Area of Science:

• Machine Learning
• Computer Vision
• Natural Language Processing

Background:

• Domain adaptation addresses challenges in applying models trained on one data distribution to another.
• Existing methods like instance reweighting and feature transformation have limitations with high-dimensional data and large domain discrepancies.

Purpose of the Study:

• To propose novel frameworks for unsupervised domain adaptation that overcome limitations of current methods.
• To adapt source domain distributions to target domain distributions within a subspace.

Main Methods:

• Modeling unsupervised domain adaptation under the generalized covariate shift assumption.
• Applying a distribution adaptation function to align source and target distributions in a subspace.
• Jointly learning the subspace distribution adaptation function and the target prediction model through Bregman-divergence-embedded structural risk minimization (BSRM) and joint structural risk minimization (JSRM).

Main Results:

• The proposed BSRM and JSRM frameworks yield convex optimization problems under specific instantiations.
• Experimental results demonstrate statistically significant improvements over state-of-the-art domain adaptation techniques.
• The methods show strong performance on both synthetic and real-world text and image datasets.

Conclusions:

• The proposed BSRM and JSRM frameworks offer effective solutions for unsupervised domain adaptation.
• These methods successfully address challenges posed by high-dimensional data and significant cross-domain discrepancies.
• The research advances domain adaptation techniques with practical implications for various data types.