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.
翻译:不仅由于各流流流流的不同分布,而且由于数据流环境的迅速变化和永无止尽,在多个流流进程中的知识转让仍然是具有挑战性的问题。尽管这一领域的研究成就不断增长,但大多数现有作品是为单一源域开发的,这限制了其利用多源域的复原力,使其无法迅速从概念漂流中恢复过来,避免负转移问题。在多源流进程下提出的在线域适应技术,即自动在线多源域适应(AOMSDA) 。AOMSDA的在线域适应战略是在一个同时采用分解自动编码的基因化和歧视性方法(DAE)下制定的。在这种方法中,基于中央时段的常规化器(CMD)被整合,以便利用互补的信息来源,处理多源域的存在。在不同时期发生的非同步概念流,即自动在线多源域适应(AOMSDA)的架构和无缝重新加权战略。我们的数字研究表明,AOMSDA/Rembiels Aral Armal Armission是A/Recommission的共享源。