Abstract:
Kernel functions support density estimation, classification, regression, and outlier detection in many applications. Online operations include weighted aggregation of kernel function values with respect to a set of points. Scalable aggregation methods for Gaussian, polynomial, sigmoid, and additive kernels and weighting schemes are still unknown. We propose a novel and effective bounding technique to accelerate kernel aggregation using index structures. We also apply our method to additive kernel functions like 2, intersection, JS, and Hellinger kernels, which are widely used in computer vision, medical science, geoscience, and other fields. We develop novel and effective bound functions to efficiently evaluate kernel aggregation for additive kernel functions. Our proposed solution KARL outperforms the state-of-the-art for different kernel functions in many real datasets.
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