Geometric graph-based Methods for High Dimensional Data

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• Andrea Bertozzi (University of California, Los Angeles)
• Tuesday 23 November 2021, 16:30-17:30
• Seminar Room 1, Newton Institute.

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MDLW04 - The power of women in deep learning

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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