Abstract Unsupervised Domain Adaptation solves knowledge transfer along with the
coexistence of well-annotated source domain and unlabeled target instances. However, the …

K-means is a fundamental clustering algorithm widely used in both academic and industrial
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …

Unsupervised domain adaptation (UDA) aims to utilize knowledge from a label-rich source
domain to understand a similar yet distinct unlabeled target domain. Notably, global …

Unsupervised domain adaptation has caught appealing attentions as it facilitates the
unlabeled target learning by borrowing existing well-established source domain knowledge …

Conventional domain adaptation methods usually resort to deep neural networks or
subspace learning to find invariant representations across domains. However, most deep …

Background Brain computer interface (BCI) utilizes brain signals to help users interact with
external devices directly. EEG is one of the most commonly used techniques for brain signal …

Cross-database micro-expression recognition (CDMER) is one of recently emerging and
interesting problems in micro-expression analysis. CDMER is more challenging than the …

Cross-domain sentiment classification uses knowledge from source domain tasks to
enhance the sentiment classification of the target task. It can reduce the workload of data …

Transfer learning has attracted great attention to facilitate the sparsely labeled or unlabeled
target learning by leveraging previously well-established source domain through knowledge …

Wireless sensing is an emerging technique which empowers wireless devices with
additional sensing ability, that is, the ability to sense the target location, activity, gesture, vital …