FALKON: An Optimal Large Scale Kernel Method

Understand

Kernel methods provide a principled way to perform non linear, nonparametric learning.

• They rely on solid functional analytic foundations and enjoy optimal statistical properties.
• However, at least in their basic form, they have limited applicability in large scale scenarios because of stringent computational requirements in terms of time and especially memory.
• In this paper, we take a substantial step in scaling up kernel methods, proposing FALKON, a novel algorithm that allows to efficiently process millions of points.