Probabilistic Graph Embedding for unsupervised domain adaptation

Unsupervised domain adaptation aims to predict unlabeled target domain data by taking advantaging of labeled source domain data. This problem is hard to solve mainly because of the limitation of target domain label and the difference between both domains. To overcome these two difficulties, many researchers have proposed to assign pseudo labels for the unlabeled target domain data and then project both domain data into a common subspace. The use of pseudo labels, however, lack of theoretical basis and thus may not gain ideal classification results eventually. Therefore, in this work we propose a novel method called Probabilistic Graph Embedding(PGE). PGE first derives probabilities that the target domain instances belong to each category, which is believed to be able to explore target domain better. We then obtain a projection matrix by constructing a within-class probabilistic graph. This projection matrix can embed both domains into a shared subspace where domain shift is largely diminished. Experiments on object recognition cross-domain datasets show that PGE is more effective and robust than the state-of-art unsupervised domain adaptation methods.