Sampling with Minimum Sum of Squared Similarities for Nystrom-Based Large Scale Spectral Clustering

The Nystrom sampling provides an efficient approach for large scale clustering problems, by generating a low-rank matrix approximation. However, existing sampling methods are limited by their accuracies and computing times. This paper proposes a scalable Nystrom-based clustering algorithm with a new sampling procedure, Minimum Sum of Squared Similarities (MSSS). Here we provide a theoretical analysis of the upper error bound of our algorithm, and demonstrate its performance in comparison to the leading spectral clustering methods that use Nystrom sampling.