Filtered Manifold Alignment
This addresses the problem of leveraging data across domains for improved learning in transfer learning, with incremental improvements in computational efficiency and flexibility.
The paper tackles domain adaptation by proposing a new semi-supervised manifold alignment technique that projects and filters source and target domains to low-dimensional spaces, achieving state-of-the-art classification accuracy on benchmark image datasets.
Domain adaptation is an essential task in transfer learning to leverage data in one domain to bolster learning in another domain. In this paper, we present a new semi-supervised manifold alignment technique based on a two-step approach of projecting and filtering the source and target domains to low dimensional spaces followed by joining the two spaces. Our proposed approach, filtered manifold alignment (FMA), reduces the computational complexity of previous manifold alignment techniques, is flexible enough to align domains with completely disparate sets of feature and demonstrates state-of-the-art classification accuracy on multiple benchmark domain adaptation tasks composed of classifying real world image datasets.