An Unsupervised Tensor-Based Domain Alignment
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915756272254976 |
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| author | Lee, Chong Hyun Lee, Kibae Yim, Hyun Hee |
| author_facet | Lee, Chong Hyun Lee, Kibae Yim, Hyun Hee |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18564 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Unsupervised Tensor-Based Domain Alignment Lee, Chong Hyun Lee, Kibae Yim, Hyun Hee Machine Learning Computer Vision and Pattern Recognition Signal Processing 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. |
| title | An Unsupervised Tensor-Based Domain Alignment |
| topic | Machine Learning Computer Vision and Pattern Recognition Signal Processing |
| url | https://arxiv.org/abs/2601.18564 |