  Domain adaptation solves image classification problems in the target domain
by taking advantage of the labelled source data and unlabelled target data.
Usually, the source and target domains share the same set of classes. As a
special case, Open-Set Domain Adaptation (OSDA) assumes there exist additional
classes in the target domain but not present in the source domain. To solve
such a domain adaptation problem, our proposed method learns discriminative
common subspaces for the source and target domains using a novel Open-Set
Locality Preserving Projection (OSLPP) algorithm. The source and target domain
data are aligned in the learned common spaces class-wisely. To handle the
open-set classification problem, our method progressively selects target
samples to be pseudo-labelled as known classes and rejects the outliers if they
are detected as from unknown classes. The common subspace learning algorithm
OSLPP simultaneously aligns the labelled source data and pseudo-labelled target
data from known classes and pushes the rejected target data away from the known
classes. The common subspace learning and the pseudo-labelled sample
selection/rejection facilitate each other in an iterative learning framework
and achieves state-of-the-art performance on benchmark datasets Office-31 and
Office-Home with the average HOS of 87.4% and 67.0% respectively.
