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Application of the optimal transport Gromov-Wasserstein problem to manifold learning and graph analysis

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If you have a question about this talk, please contact Dr H Ge .

I will present some of my recent PhD research, where we reexamine unsupervised learning methods through the perspective of distributions using optimal transport. By drawing connections with the Gromov-Wasserstein (GW) problem, this research introduces a new comprehensive framework called distributional reduction. This framework encompasses both dimensionality reduction (DR) and clustering as special cases, enabling to address them jointly within a single optimization problem. Additionally, I will discuss the applications of the GW problem as a similarity measure between structured data represented as distributions, typically lying in different metric spaces, such as graphs of varying sizes. This will naturally lead to exploring research directions in graph generative modeling using GW.

This talk is part of the Machine Learning @ CUED series.

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