Unsupervised domain adaptation using manifold alignment for object and event categorization

This paper describes a method of cross-domain object and event categorization, using the concept of domain adaptation. Here, a classifier is trained using samples from the source/ auxiliary domain and performance is observed on a set of test samples taken from a different domain, termed as the target domain. To overcome the difference between the two domains, we aim to find an optimal sub-space such that the instances from both the domains follow similar distributions when projected onto the sub-space. Along with the distributions, the underlying manifolds of the two domains are aligned in the sub-space to reduce the difference in structure of the data from the two domains. The local spatial arrangement of the instances in both the domains are also preserved in the optimal sub-space. Results show that the proposed method of unsupervised domain adaptation provides better classification accuracy than a few state of the art methods.