  In leveraging manifold learning in domain adaptation (DA), graph
embedding-based DA methods have shown their effectiveness in preserving data
manifold through the Laplace graph. However, current graph embedding DA methods
suffer from two issues: 1). they are only concerned with preservation of the
underlying data structures in the embedding and ignore sub-domain adaptation,
which requires taking into account intra-class similarity and inter-class
dissimilarity, thereby leading to negative transfer; 2). manifold learning is
proposed across different feature/label spaces separately, thereby hindering
unified comprehensive manifold learning. In this paper, starting from our
previous DGA-DA, we propose a novel DA method, namely Attention Regularized
Laplace Graph-based Domain Adaptation (ARG-DA), to remedy the aforementioned
issues. Specifically, by weighting the importance across different sub-domain
adaptation tasks, we propose the Attention Regularized Laplace Graph for
class-aware DA, thereby generating the attention regularized DA. Furthermore,
using a specifically designed FEEL strategy, our approach dynamically unifies
alignment of the manifold structures across different feature/label spaces,
thus leading to comprehensive manifold learning. Comprehensive experiments are
carried out to verify the effectiveness of the proposed DA method, which
consistently outperforms the state-of-the-art DA methods on 7 standard DA
benchmarks, i.e., 37 cross-domain image classification tasks including object,
face, and digit images. An in-depth analysis of the proposed DA method is also
discussed, including sensitivity, convergence, and robustness.
