On the Numerical Rank of Radial Basis Function Kernels in High Dimensions
On the Numerical Rank of Radial Basis Function Kernels in High Dimensions
Low-rank approximations are popular methods to reduce the high computational cost of algorithms involving large-scale kernel matrices. The success of low-rank methods hinges on the matrix rank of the kernel matrix, and in practice, these methods are effective even for high-dimensional datasets. Their practical success motivates our analysis of the …