Theoretical analysis of the Minimum Sum of Squared Similarities sampling for Nyström-based spectral clustering

Abstract

Spectral clustering has shown a superior performance in analyzing the cluster structure. However, the exponentially computational complexity limits its application in analyzing large-scale data. To tackle this problem, many low-rank matrix approximating algorithms are proposed, of which the Nystrom method is an approach with proved lower approximate errors. The algorithms commonly combine two powerful techniques in machine learning: spectral clustering algorithms and Nystrom methods commonly used to obtain good quality low rank approximations of large matrices. This paper proposes to analyze a scalable Nystrom-based clustering algorithm with a Minimum Sum of Squared Similarities (MSSS) sampling procedure. We provide theoretical analysis of the performance of the algorithm MSSS and demonstrate its theoretical performance in comparison to the leading spectral clustering methods that use Nystrom sampling.