This paper describes a new method of unsupervised domain adaptation (DA) using the properties of the sub-spaces spanning the source and target domains, when projected along a path in the Grassmann manifold. Our proposed method uses both the geometrical and the statistical properties of the subspaces spanning the two domains to estimate a sequence of optimal intermediary subspaces. This creates a path of shortest length between the sub-spaces of source and target domains, where the distributions of the projected source and target domain data are identical when projected onto these intermediate sub-spaces (lying along the path). We extend our concept to the kernel space and perform non-linear projections on the subspaces using kernel trick. Projections of the source and target domains onto these intermediary sub-spaces are used to obtain the incremental (or gradual) change in the geometrical as well as the statistical properties of sub-spaces spanning the source and target domains. Results on object and event categorization using real-world datasets, show that our proposed optimal path in the Grassmann manifold produces better results for the problem of DA than the usual geodesic path.
Samanta et al. in REF describe a new method of unsupervised domain adaptation (DA) using the properties of the sub-spaces spanning the source and target domains, when projected along a path in the Grassmann manifold.
Modeling Sequential Domain Shift through Estimation of Optimal Sub-spaces for Categorization.
BMVC
Computer Science