Date: Tuesday, October 15, 2019
Location: Rockefeller Hall 300
TItle: Geometric Graph-based Methods for High Dimensional Data
Abstract: Abstract: We present new methods for segmentation of large datasets with graph-based structure. The methods make parallels between geometric ideas in Euclidean space such as motion by mean curvature, ported to a graphical framework, and can be made rigorous. We show diverse examples including image processing applications such as image and video labeling and hyperspectral video segmentation, and machine learning and community detection in social networks, including modularity optimization posed as a graph total variation minimization problem.