Kernelized Bures metric: A framework for effective domain adaptation in sensor data analysis

Abstract

Unsupervised Domain Adaptation (UDA) plays a crucial role in enabling the transfer of models trained on labeled source domains to unlabeled target domains. However, this transferability encounters significant challenges when dealing with intricate time series models due to the complex temporal dynamics that differ between domains. These divergent dynamics result in disparities in time and frequency representations, leading to misalignments and gaps. Furthermore, the absence of localized information capture in previous approaches undermines the performance potential of the models. We introduce the KBSDA (Kernel Bures Sub-Domain Adaptation) approach to tackle the issues above. Our methodology employs the fast Fourier transform to extract frequency features while encompassing the time features. Our approach’s key focus is capturing intricate attributes containing local information, a task accomplished by applying the Local Maximum Mean Discrepancy (LMMD) metric. Furthermore, we address the distribution gaps between the source and target domains, elevating the alignment process by incorporating the kernel Bures metric. This metric, which encompasses higher-order moments and effectively manages intricate non-linear relationships, significantly manages complex distribution shifts. To validate the effectiveness of our approach, we conducted comprehensive experiments on well-established time series domain adaptation datasets such as HAR, HHAR, WISDM, and SSC. Our approach shows average accuracy of 95.10%, 78.04%, 78.69%, and 74.25% for HAR, HHAR, WISDM, and SSC, respectively.