Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning
This addresses the problem of adapting multiple source domains to a streaming target in transfer learning, which is incremental as it builds on existing methods.
The paper tackled online multi-source domain adaptation for streaming target data by introducing a novel approach using Gaussian Mixture Models and dataset dictionary learning, achieving on-the-fly adaptation on the Tennessee Eastman Process benchmark.
This paper addresses the challenge of online multi-source domain adaptation (MSDA) in transfer learning, a scenario where one needs to adapt multiple, heterogeneous source domains towards a target domain that comes in a stream. We introduce a novel approach for the online fit of a Gaussian Mixture Model (GMM), based on the Wasserstein geometry of Gaussian measures. We build upon this method and recent developments in dataset dictionary learning for proposing a novel strategy in online MSDA. Experiments on the challenging Tennessee Eastman Process benchmark demonstrate that our approach is able to adapt \emph{on the fly} to the stream of target domain data. Furthermore, our online GMM serves as a memory, representing the whole stream of data.