Geometric graph-based methods for high dimensional data

Monday April 6, 2015 3:00 PM

Computing + Mathematical Sciences Seminar

Speaker: Andrea Bertozzi, University of California Los Angeles
Location: Annenberg 213

We present new methods for segmentation of large datasets with graph based structure. The method combines ideas from classical nonlinear PDE-based image segmentation with fast and accessible linear algebra methods for computing information about the spectrum of the graph Laplacian. The goal of the algorithms is to solve semi-supervised and unsupervised graph cut optimization problems. I will present results for image processing applications such as image labeling and hyperspectral video segmentation, and results from machine learning and community detection in social networks, including modularity optimization posed as a graph total variation minimization problem.

Series Computing and Mathematical Sciences Colloquium Series

Contact: Sheila Shull at 626.395.4560. sheila@cms.caltech.edu