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 (CSS) 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. In this study, we show that CSS can be used to compute spectral clustering and use this to obtain new graph clustering algorithms.