Aug 17, 2017 · View a PDF of the paper titled An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning, by Felix P.

Dec 31, 2018 · ABSTRACT Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data ...

Jan 11, 2024 · PDF | Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data.

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 ...

We started by demonstrating that manifold learning techniques and, in particular, Diffusion Maps, can be used to reconstruct the topology of the physical space ...

This work validates this “emergent space” reconstruction for time series sampled without space labels in known PDEs, and discusses how data-driven “spatial” ...

“An Emergent Space for Distributed Data With Hidden Internal Order through Manifold Learning”. IEEE Access 6: 77402-13. Publisher's Version: An Emergent Space ...

An emergent space for distributed data with hidden internal order through manifold learning ... order: Observability, gauge invariance and manifold learning.

Nov 25, 2022 · An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning. #1. Felix P. Kemeth. ,. Sindre W. Haugland. ,.

Publisher's Version: An equal space for complex data with unknown internal order: observability, gauge invariance and manifold learning (Link is external).