An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning.

ABSTRACT: Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, useful in nonlinear model identification tasks. We focus here on the case of time series data that can ultimately be modelled as a spatially distributed system (e.g. a partial differential equation, PDE), but where we do not know the space in which this PDE should be formulated. Hence, even the spatial coordinates for the distributed system themselves need to be identified - to "emerge from"-the data mining process. We will first validate this "emergent space" reconstruction for time series sampled without space labels in known PDEs; this brings up the issue of observability of physical space from temporal ob

SUBMITTER: Kemeth FP 

PROVIDER: S-EPMC6553659 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature