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Geometric graph-based methods for high dimensional data

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  • UserAndrea Bertozzi (Mathematics, UCLA) World_link
  • ClockMonday 02 November 2015, 16:00-17:00
  • HouseMR 13, CMS.

If you have a question about this talk, please contact Carola-Bibiane Schoenlieb.

This is a joint GAPDE & ACA seminar

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 Applied and Computational Analysis series.

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