Homologous Component Analysis for Domain Adaptation.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Science

Background:

• Domain adaptation methods often use a single transformation, risking information loss like variance and neighborhood relationships.
• Preserving conditional distributions while aligning marginal distributions is key in domain adaptation.

Purpose of the Study:

• To propose a novel domain adaptation method, Homologous Component Analysis (HCA), that utilizes two distinct yet homologous transformations.
• To address the information loss issue inherent in single-transformation domain adaptation techniques.

Main Methods:

• HCA employs two unique transformations to align distributions, incorporating side information for enhanced preservation of conditional distributions.
• The optimization problem is solved using the Alternating Direction Minimizing Method (ADMM) within Stiefel manifolds.
• A generalization error bound for semi-supervised domain adaptation is derived, demonstrating the benefit of dual transformations.

Main Results:

• Experiments on synthetic and real data show HCA outperforms state-of-the-art methods in classification accuracy.
• Numerical evidence confirms the distinctness of the learned optimal transformations using chordal and Frobenius distances.
• The proposed dual-transformation approach effectively reduces the upper bound of the generalization error.

Conclusions:

• Homologous Component Analysis (HCA) offers a superior domain adaptation strategy by leveraging dual transformations.
• The method effectively preserves essential data characteristics, leading to improved performance and reduced error bounds in domain adaptation tasks.