  Domain adaptation (DA) addresses the real-world image classification problem
of discrepancy between training (source) and testing (target) data
distributions. We propose an unsupervised DA method that considers the presence
of only unlabelled data in the target domain. Our approach centers on finding
matches between samples of the source and target domains. The matches are
obtained by treating the source and target domains as hyper-graphs and carrying
out a class-regularized hyper-graph matching using first-, second- and
third-order similarities between the graphs. We have also developed a
computationally efficient algorithm by initially selecting a subset of the
samples to construct a graph and then developing a customized optimization
routine for graph-matching based on Conditional Gradient and Alternating
Direction Multiplier Method. This allows the proposed method to be used widely.
We also performed a set of experiments on standard object recognition datasets
to validate the effectiveness of our framework over state-of-the-art
approaches.
