Multi-Source Domain Adaptation Using Ambient Sensor Data

Abstract

Smart buildings have gained increasing interest recently by providing several advanced solutions, especially AI-based solutions. Activity recognition and occupancy estimation are among the outcomes of smart buildings that can help provide several advantages such as energy management and security solutions. Previously, domain adaptation (DA) has been widely considered by researchers to transfer knowledge from source domains, where we have abundant labeled data, to a target domain where labeled data is scarce. It is a tedious and time-consuming task to label data, especially with smart building applications which is why researchers have considered unsupervised DA where we do have labeled data in the source domain and unlabeled data in the target domain. Semi-supervised DA (SSDA) adaptation has also been considered by researchers where we have a small amount of labeled data in the target domain. Most unsupervised DA (UDA) and SSDA methods transfer knowledge from one source to one target. However, it is possible to exploit knowledge from multiple source domains instead of one single domain to enhance the performance of the target domain. Multi-source DA (MSDA) is more difficult than single-source DA but also it is more efficient. In this research, we adapt several MDSA methods and evaluate them using sensorial datasets.