Speaker: Xuan Wu
Affiliation: Johns Hopkins University

Title: Coreset for Clustering in Graph Metrics.

Abstract:
Clustering is a fundamental task in machine learning. As the increasing demand for running machine learning algorithms in the huge data sets, classic clustering algorithms were found not to scale well. To this end, coreset is introduced as a powerful data reduction technique that turns a huge dataset into a tiny proxy. Coresets have been also successfully applied to streaming and distributed computing. Coresets for clustering in Euclidean spaces have been very well studied. However, very few results were known about the non-Euclidean space. Beyond Euclidean, graph metrics is a very important family of metric space. In this talk, I will cover my recent work on coreset for k-clustering in graph metrics, including bounded treewidth graph and excluded-minor graph.