An Unsupervised Tensor-Based Domain Alignment
This addresses domain adaptation problems for machine learning applications where data distributions differ, offering a more flexible and robust solution, though it appears incremental as it generalizes existing tensor-based methods.
The paper tackles domain adaptation by proposing a tensor-based algorithm that aligns source and target tensors in an invariant subspace using iterative optimization on an oblique manifold, resulting in enhanced conversion speed and significantly boosted classification accuracy compared to state-of-the-art methods.
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.