Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes

Frank Schoeneman; Varun Chandola; Nils Napp; Olga Wodo; Jaroslaw Zola

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arXiv · 1802.06823

Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes

Frank Schoeneman·Varun Chandola·Nils Napp·Olga Wodo·Jaroslaw Zola

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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.

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Frank Schoeneman, Varun Chandola, Nils Napp, Olga Wodo, Jaroslaw Zola. 2018-02-19. Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes. https://arxiv.org/abs/1802.06823

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