  We present a novel unsupervised domain adaptation (DA) method for
cross-domain visual recognition. Though subspace methods have found success in
DA, their performance is often limited due to the assumption of approximating
an entire dataset using a single low-dimensional subspace. Instead, we develop
a method to effectively represent the source and target datasets via a
collection of low-dimensional subspaces, and subsequently align them by
exploiting the natural geometry of the space of subspaces, on the Grassmann
manifold. We demonstrate the effectiveness of this approach, using empirical
studies on two widely used benchmarks, with state of the art domain adaptation
performance
