Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning

Hence, incremental DA is a good candidate to enhance the performance of automatic fault diagnosis systems. This paper addresses the challenge of online multi-source In the context of DA, a prominent framework is Optimal domain adaptation (MSDA) in transfer learning, a scenario Transport (OT) [4, 5], which is a mathematical theory where one needs to adapt multiple, heterogeneous source concerned with the displacement of mass at least effort. In domains towards a target domain that comes in a stream. We this paper, we are particularly interested in the Dataset Dictionary introduce a novel approach for the online fit of a Gaussian Learning (DaDiL) framework proposed by [6], especially Mixture Model (GMM), based on the Wasserstein geometry its Gaussian Mixture Model (GMM) formulation [7], of Gaussian measures. We build upon this method and recent which learns to interpolate probability measures in a Wasserstein developments in dataset dictionary learning for proposing a space through dictionary learning.