  We address the problem of visual knowledge adaptation by leveraging labeled
patterns from source domain and a very limited number of labeled instances in
target domain to learn a robust classifier for visual categorization. This
paper proposes a new extreme learning machine based cross-domain network
learning framework, that is called Extreme Learning Machine (ELM) based Domain
Adaptation (EDA). It allows us to learn a category transformation and an ELM
classifier with random projection by minimizing the l_(2,1)-norm of the network
output weights and the learning error simultaneously. The unlabeled target
data, as useful knowledge, is also integrated as a fidelity term to guarantee
the stability during cross domain learning. It minimizes the matching error
between the learned classifier and a base classifier, such that many existing
classifiers can be readily incorporated as base classifiers. The network output
weights cannot only be analytically determined, but also transferrable.
Additionally, a manifold regularization with Laplacian graph is incorporated,
such that it is beneficial to semi-supervised learning. Extensively, we also
propose a model of multiple views, referred as MvEDA. Experiments on benchmark
visual datasets for video event recognition and object recognition, demonstrate
that our EDA methods outperform existing cross-domain learning methods.
