Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes
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Scientific and engineering processes produce massive high-dimensional data sets that are generated as highly non-linear transformations of an initial state and few process parameters. Mapping such data to a low-dimensional manifold can facilitate better understanding of the underlying process, and ultimately their optimization. We show that off-the-shelf non-linear spectral dimensionality methods, such as Isomap, fail for such data, primarily due to the presence of strong temporal correlation among observations belonging to the same process pathways. We propose a novel method, Entropy-Isomap, to address this issue. The proposed method is successfully applied to morphology evolution data of the organic thin film fabrication process. The resulting output is ordered by the process variables. It allows for low-dimensional visualization of the morphological pathways, and provides key insights to guide subsequent design and exploration.
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