Research Frontiers

In this book, we have delved into the realm of dynamic learning for dimension reduction and data clustering. Our exploration begins by highlighting the pressing need for a dynamic graph learning framework in the context of big data environments. We then delve into the challenges faced by dynamic graph learning when applied to dimension reduction and clustering tasks, including joint optimization, accurate modeling of data correlations with graphs, multi-view fusion, and out-of-sample extension.