  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.
