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

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

2019-11-19 | |  70 |   41 |   0
Abstract The Nystro?m 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 Nystro?m-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 Nystro?m sampling.

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