Revisiting the Nystrom Method for Improved Large-Scale Machine Learning

7 Mar 2013Alex GittensMichael W. Mahoney

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 projection methods on a diverse suite of SPSD matrices... (read more)

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