An Unsupervised Tensor-Based Domain Alignment

2026-01-26

AI Summary

This work addresses the challenge of distributional discrepancy between source and target domains in unsupervised tensor domain adaptation by proposing a novel method that jointly optimizes alignment matrices and a shared invariant subspace. By imposing constraints on the more flexible oblique manifold—rather than the conventional Stiefel manifold—and incorporating a variance-preserving regularizer to enhance robustness, the proposed framework generalizes existing tensor alignment approaches while significantly improving both domain adaptation efficiency and classification accuracy. Extensive experiments demonstrate that the method consistently outperforms state-of-the-art techniques across multiple benchmark datasets.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

We propose a tensor-based domain alignment (DA) algorithm designed to align source and target tensors within an invariant subspace through the use of alignment matrices. These matrices along with the subspace undergo iterative optimization of which constraint is on oblique manifold, which offers greater flexibility and adaptability compared to the traditional Stiefel manifold. Moreover, regularization terms defined to preserve the variance of both source and target tensors, ensures robust performance. Our framework is versatile, effectively generalizing existing tensor-based DA methods as special cases. Through extensive experiments, we demonstrate that our approach not only enhances DA conversion speed but also significantly boosts classification accuracy. This positions our method as superior to current state-of-the-art techniques, making it a preferable choice for complex domain adaptation tasks.

Problem

• domain adaptation
• tensor alignment
• unsupervised learning
• invariant subspace
• cross-domain

Innovation

• tensor-based domain alignment
• oblique manifold optimization
• variance-preserving regularization
• unsupervised domain adaptation
• invariant subspace learning