Semi-Supervised Domain Adaption Classifier via Broad Learning System

Broad Learning System (BLS) is effective and efficient in dealing with various machine learning problems. When constructing a BLS based classifier with domain adaption capability, the mapped features and enhancement nodes are determined by random parameters. Thus, there will be discrepancy between the hidden layer features of the source domain and target domain. Therefore, we propose a new semi-supervised classifier with domain adaption capability based on BLS. Firstly, in order to reduce the discrepancy between the hidden layer features of domains, our method aligns the second-order statistics of mapped features due to the fact that enhancement nodes are generated by mapped features. Further, when learning the classifier through the aligned features, we embed balanced distribution adaptation to improve domain adaption capability of the classifier. Experiments on benchmark datasets demonstrate our method has better classification accuracy than some existences.