Kernelized approaches to streaming compression of scientific data

Abstract

In this paper three algorithms are developed for the streaming compression of scientific data. The algorithms presented are reliant on the theory of vector-valued reproducing kernel Hilbert spaces and operator valued kernels. The scientific data is modeled as a snapshot of time dependent vector-field F(x,t) over a manifold M and the recovery of the data is framed as a learning problem. These processes are then appropriately modified and analyzed for the streaming scenario in which data is generated without the ability to revisit past entries.