Revisiting the Nystrom Method for Improved Large-Scale Machine Learning

JOURNAL OF MACHINE LEARNING RESEARCH, pp. 117:1-117:65, 2016.

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Abstract:

We reconsider randomized algorithms for the low-rank approximation of symmetric positive semi-definite (SPSD) matrices such as Laplacian and kernel matrices that arise in data analysis and machine learning applications. Our main results consist of an empirical evaluation of the performance quality and running time of sampling and projecti...More

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