Fast Learning in Reproducing Kernel Krein Spaces via Generalized Measures

In this paper, we attempt to solve a long-lasting open question in non-positive definite (non-PD) kernels: does a given non-PD kernel can be decomposed into the difference of two PD kernels (termed as positive decomposition)? We consider this question in a distribution view by introducing the \emph{signed measure}, which transforms positive decomposition to measure decomposition: a series of non-PD kernels can be associated with the linear combination of specific finite Borel measures... (read more)

PDF Abstract
No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper