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Large scale online kernel learning aims to build an efficient and scalablekernel-based predictive model incrementally from a sequence of potentiallyinfinite data points. A current key approach focuses on ways to produce anapproximate finite-dimensional feature map, assuming that the kernel used has afeature map with intractable dimensionality---an assumption traditionally heldin kernel-based methods. While this approach can deal with large scale datasetsefficiently, this outcome is achieved by compromising predictive accuracybecause of the approximation. We offer an alternative approach which overridesthe assumption and puts the kernel used at the heart of the approach. Itfocuses on creating an exact, sparse and finite-dimensional feature map of akernel called Isolation Kernel. Using this new approach, to achieve the aboveaim of large scale online kernel learning becomes extremely simple---simply useIsolation Kernel instead of a kernel having a feature map with intractabledimensionality. We show that, using Isolation Kernel, large scale online kernellearning can be achieved efficiently without sacrificing accuracy.

Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learning