Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learning
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Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. To achieve this aim, the method must be able to deal with a potentially infinite number of support vectors. The current state-of-the-art is unable to deal with even a moderate number of support vectors. This paper identifies the root cause of the current methods, i.e., the type of kernel used which has a feature map of infinite dimensionality. With this revelation and together with our discovery that a recently introduced Isolation Kernel has a finite feature map, to achieve the above aim of large scale online kernel learning becomes extremely simple---simply use Isolation Kernel instead of kernels having infinite feature map. We show for the first time that online kernel learning is able to deal with a potentially infinite number of support vectors.
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