  Domain adaptation (DA) is transfer learning which aims to leverage labeled
data in a related source domain to achieve informed knowledge transfer and help
the classification of unlabeled data in a target domain. In this paper, we
propose a novel DA method, namely Robust Data Geometric Structure Aligned,
Close yet Discriminative Domain Adaptation (RSA-CDDA), which brings closer, in
a latent joint subspace, both source and target data distributions, and aligns
inherent hidden source and target data geometric structures while performing
discriminative DA in repulsing both interclass source and target data. The
proposed method performs domain adaptation between source and target in solving
a unified model, which incorporates data distribution constraints, in
particular via a nonparametric distance, i.e., Maximum Mean Discrepancy (MMD),
as well as constraints on inherent hidden data geometric structure segmentation
and alignment between source and target, through low rank and sparse
representation. RSA-CDDA achieves the search of a joint subspace in solving the
proposed unified model through iterative optimization, alternating Rayleigh
quotient algorithm and inexact augmented Lagrange multiplier algorithm.
Extensive experiments carried out on standard DA benchmarks, i.e., 16
cross-domain image classification tasks, verify the effectiveness of the
proposed method, which consistently outperforms the state-of-the-art methods.
