Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning
<|reference_start|>Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning: 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.<|reference_end|>
@article{montesuma2024online,
    title={Online Multi-Source Domain Adaptation through Gaussian Mixtures and
  Dataset Dictionary Learning},
    author={Eduardo Fernandes Montesuma, Stevan Le Stanc, Fred Ngol`e Mboula},
    journal={arXiv preprint arXiv:2407.19853},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19853},
    primaryClass={cs.LG stat.ML}
}