Improved Coresets for Kernel Density Estimates
This work addresses efficient data compression for kernel density estimation, with incremental improvements in theoretical bounds for specific kernels.
The paper tackles the problem of approximating kernel density estimates with smaller coresets, achieving an L∞ error bound of ε with coreset size 2/ε² for characteristic kernels, independent of dimension and other data aspects, and shows this is tight in unrestricted dimensions while improving bounds for constant dimensions.
We study the construction of coresets for kernel density estimates. That is we show how to approximate the kernel density estimate described by a large point set with another kernel density estimate with a much smaller point set. For characteristic kernels (including Gaussian and Laplace kernels), our approximation preserves the $L_\infty$ error between kernel density estimates within error $ε$, with coreset size $2/ε^2$, but no other aspects of the data, including the dimension, the diameter of the point set, or the bandwidth of the kernel common to other approximations. When the dimension is unrestricted, we show this bound is tight for these kernels as well as a much broader set. This work provides a careful analysis of the iterative Frank-Wolfe algorithm adapted to this context, an algorithm called \emph{kernel herding}. This analysis unites a broad line of work that spans statistics, machine learning, and geometry. When the dimension $d$ is constant, we demonstrate much tighter bounds on the size of the coreset specifically for Gaussian kernels, showing that it is bounded by the size of the coreset for axis-aligned rectangles. Currently the best known constructive bound is $O(\frac{1}ε \log^d \frac{1}ε)$, and non-constructively, this can be improved by $\sqrt{\log \frac{1}ε}$. This improves the best constant dimension bounds polynomially for $d \geq 3$.