Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective. (arXiv:2112.09893v2 [cs.LG] UPDATED)

Matrix approximations are a key element in large-scale algebraic machine
learning approaches. The recently proposed method MEKA (Si et al., 2014)
effectively employs two common assumptions in Hilbert spaces: the low-rank
property of an inner product matrix obtained from a shift-invariant kernel
function and a data compactness hypothesis by means of an inherent
block-cluster structure. In this work, we extend MEKA to be applicable not only
for shift-invariant kernels but also for non-stationary kernels like polynomial
kernels and an extreme …

arxiv kernel learning perspective