  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.
