  Knowledge transfer across several streaming processes remain challenging
problem not only because of different distributions of each stream but also
because of rapidly changing and never-ending environments of data streams.
Albeit growing research achievements in this area, most of existing works are
developed for a single source domain which limits its resilience to exploit
multi-source domains being beneficial to recover from concept drifts quickly
and to avoid the negative transfer problem. An online domain adaptation
technique under multisource streaming processes, namely automatic online
multi-source domain adaptation (AOMSDA), is proposed in this paper. The online
domain adaptation strategy of AOMSDA is formulated under a coupled generative
and discriminative approach of denoising autoencoder (DAE) where the central
moment discrepancy (CMD)-based regularizer is integrated to handle the
existence of multi-source domains thereby taking advantage of complementary
information sources. The asynchronous concept drifts taking place at different
time periods are addressed by a self-organizing structure and a node
re-weighting strategy. Our numerical study demonstrates that AOMSDA is capable
of outperforming its counterparts in 5 of 8 study cases while the ablation
study depicts the advantage of each learning component. In addition, AOMSDA is
general for any number of source streams. The source code of AOMSDA is shared
publicly in https://github.com/Renchunzi-Xie/AOMSDA.git.
