Extracting hidden patterns of multiview data containing heterogeneous feature representations is attracting more and more attention in various scientific fields such as image processing and natural language processing. In this talk we will present a comprehensive unsupervised framework that leverages existing and novel multiview learning models, towards obtaining a single node embedding from a collection of node embeddings, combining the best of all worlds. Furthermore, we will introduce two new approaches for discriminative knowledge discovery of multiple datasets and demonstrate their effectiveness on several applications such as extracting the symptoms mostly related to COVID-19 with respect to Flu using Google Trends data.

Prof. Jia Chen