Event Date
SPEAKER: Thomas Strohmer, Mathematics, UC Davis
TITLE: “A Rigorous Framework for Data Clustering”
ABSTRACT: Organizing data into meaningful groups is one of the most fundamental tasks in data analysis and machine learning. While spectral clustering has become one of the most popular clustering techniques, a rigorous and meaningful theoretical justification has still been elusive so far. I will propose a convex relaxation approach, which gives rise to a rigorous theoretical analysis of spectral clustering. We do this by deriving deterministic bounds of finding optimal graph cuts via a natural and intuitive proximity condition related to the spectrum of the graph Laplacian.
Moreover, the proposed approach provides theoretical guarantees for community detection. I will discuss extensions and applications of our framework.
DATE: Thursday, November 7th, 4:10pm
LOCATION: MSB 1147, Colloquium Room
REFRESHMENTS: 3:45pm MSB 4110 (4th floor lounge)