Graph Clustering Methods Derived from Column Subset Selection (Student Abstract)

Spectral clustering is a powerful clustering technique. It leverages the spectral properties of graphs to partition data points into meaningful clusters. The most common criterion for evaluating multi-way spectral clustering is NCut. Column Subset Selection is an important optimization technique in the domain of feature selection and dimension reduction which aims to identify a subset of columns of a given data matrix that can be used to approximate the entire matrix. We show that column subset selection can be used to compute spectral clustering and use this to obtain new graph clustering algorithms.

Authors

• Wei Mao University of Texas at Dallas
• Guihong Wan Departments of Biostatistics and Epidemiology, Harvard T. H. Chan School of Public Health, MA, USA Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, MA, USA
• Haim Schweitzer Department of Computer Science, University of Texas at Dallas, Texas, USA

Keywords

• Approximation Error
• Column Subset Selection
• Edge Matrix
• NCut
• Spectral Clustering