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Broad learning system (BLS) has been proposed for a few years. Itdemonstrates an effective learning capability for many classification andregression problems. However, BLS and its improved versions are mainly used todeal with unsupervised, supervised and semi-supervised learning problems in asingle domain. As far as we know, a little attention is paid to thecross-domain learning ability of BLS. Therefore, we introduce BLS into thefield of transfer learning and propose a novel algorithm called domainadaptation broad learning system based on locally linear embedding (DABLS-LLE).The proposed algorithm can learn a robust classification model by using a smallpart of labeled data from the target domain and all labeled data from thesource domain. The proposed algorithm inherits the computational efficiency andlearning capability of BLS. Experiments on benchmark dataset(Office-Caltech-10) verify the effectiveness of our approach. The results showthat our approach can get better classification accuracy with less running timethan many existing transfer learning approaches. It shows that our approach canbring a new superiority for BLS.

Domain Adaptation Broad Learning System Based on Locally Linear Embedding