Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes
Abstract
Scientific and engineering processes deliver massive high-dimensional data sets that are generated as non-linear transformations of an initial state and few process parameters. Mapping such data to a low-dimensional manifold facilitates better understanding of the underlying processes, and enables their optimization. In this paper, we first show that off-the-shelf non-linear spectral dimensionality reduction methods, e.g., Isomap, fail for such data, primarily due to the presence of strong temporal correlations. Then, we propose a novel method, Entropy-Isomap, to address the issue. The proposed method is successfully applied to large data describing a fabrication process of organic materials. The resulting low-dimensional representation correctly captures process control variables, allows for low-dimensional visualization of the material morphology evolution, and provides key insights to improve the process.
Cite
@article{arxiv.1802.06823,
title = {Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes},
author = {Frank Schoeneman and Varun Chandola and Nils Napp and Olga Wodo and Jaroslaw Zola},
journal= {arXiv preprint arXiv:1802.06823},
year = {2018}
}