Learning Manifolds from Dynamic Process Data †
1
Department of Computer Science & Engineering, University at Buffalo, Buffalo, NY 14260, USA
2
Department of Computational and Data-Enabled Science & Engineering, University at Buffalo, Buffalo, NY 14260, USA
3
Department of Materials Design & Innovation, University at Buffalo, Buffalo, NY 14260, USA
4
Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Proceedings of the 2018 IEEE International Conference on Big Data, Seattle, WA, USA, 10–13 December 2018.
Algorithms 2020, 13(2), 30; https://doi.org/10.3390/a13020030
Received: 30 September 2019 / Revised: 16 December 2019 / Accepted: 14 January 2020 / Published: 21 January 2020
(This article belongs to the Special Issue Algorithms for Manifold Learning and Its Applications)
Scientific data, generated by computational models or from experiments, are typically results of nonlinear interactions among several latent processes. Such datasets are typically high-dimensional and exhibit strong temporal correlations. Better understanding of the underlying processes requires mapping such data to a low-dimensional manifold where the dynamics of the latent processes are evident. While nonlinear spectral dimensionality reduction methods, e.g., Isomap, and their scalable variants, are conceptually fit candidates for obtaining such a mapping, the presence of the strong temporal correlation in the data can significantly impact these methods. In this paper, we first show why such methods fail when dealing with dynamic process data. A novel method, Entropy-Isomap, is proposed to handle this shortcoming. We demonstrate the effectiveness of the proposed method in the context of understanding the fabrication process of organic materials. The resulting low-dimensional representation correctly characterizes the process control variables and allows for informative visualization of the material morphology evolution.
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Keywords:
manifold learning; time series; dynamic processes
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MDPI and ACS Style
Schoeneman, F.; Chandola, V.; Napp, N.; Wodo, O.; Zola, J. Learning Manifolds from Dynamic Process Data. Algorithms 2020, 13, 30. https://doi.org/10.3390/a13020030
AMA Style
Schoeneman F, Chandola V, Napp N, Wodo O, Zola J. Learning Manifolds from Dynamic Process Data. Algorithms. 2020; 13(2):30. https://doi.org/10.3390/a13020030
Chicago/Turabian StyleSchoeneman, Frank; Chandola, Varun; Napp, Nils; Wodo, Olga; Zola, Jaroslaw. 2020. "Learning Manifolds from Dynamic Process Data" Algorithms 13, no. 2: 30. https://doi.org/10.3390/a13020030
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