  Domain adaptation (DA) aims to enable a learning model trained from a source
domain to generalize well on a target domain, despite the mismatch of data
distributions between the two domains. State-of-the-art DA methods have so far
focused on the search of a latent shared feature space where source and target
domain data can be aligned either statistically and/or geometrically. In this
paper, we propose a novel unsupervised DA method, namely Discriminative Noise
Robust Sparse Orthogonal Label Regression-based Domain Adaptation (DOLL-DA).
The proposed DOLL-DA derives from a novel integrated model which searches a
shared feature subspace where source and target domain data are, through
optimization of some repulse force terms, discriminatively aligned
statistically, while at same time regresses orthogonally data labels thereof
using a label embedding trick. Furthermore, in minimizing a novel Noise Robust
Sparse Orthogonal Label Regression(NRS_OLR) term, the proposed model explicitly
accounts for data outliers to avoid negative transfer and introduces the
property of sparsity when regressing data labels.
  Due to the character restriction. Please read our detailed abstract in our
paper.
