Semi-Supervised Domain Adaptation via Asymmetric Joint Distribution Matching

Area of Science:

• Machine Learning
• Computer Science
• Artificial Intelligence

Background:

• Domain adaptation aims to transfer knowledge from a source to a target domain.
• A key challenge is the joint distribution mismatch between domains.
• This problem persists in semi-supervised domain adaptation (SSDA).

Purpose of the Study:

• To address the unsolved problem of joint distribution mismatch in SSDA.
• To propose an effective method for aligning source and target domain distributions.
• To enable robust knowledge transfer in semi-supervised learning scenarios.

Main Methods:

• Developed an asymmetric joint distribution matching (AJDM) approach.
• Utilized asymmetric matrices to linearly match joint distributions via relative chi-square divergence.
• Introduced a least square method for divergence estimation, avoiding direct distribution calculation.
• Extended AJDM to a kernel version for handling nonlinear data.
• Formulated linear and nonlinear mapping as Riemannian manifold optimization problems.

Main Results:

• The proposed AJDM approach effectively matches source and target joint distributions.
• The kernelized AJDM successfully handles nonlinear data relationships.
• Numerical experiments confirmed the approach's effectiveness on synthetic and real-world datasets.
• AJDM demonstrated superiority over existing SSDA techniques.

Conclusions:

• AJDM provides a novel solution for joint distribution matching in SSDA.
• The method facilitates accurate knowledge transfer by aligning domain distributions.
• The generalized kernel version enhances applicability to complex, nonlinear data.
• AJDM represents a significant advancement in semi-supervised domain adaptation.