Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes
复制标题
熵-Isomap：高维动态过程的流形学习
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Zola
中科院分区：
文献类型：
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作者：
Frank Schoeneman;V. Chandola;N. Napp;O. Wodo;J. Zola
科学和工程过程提供了大量的高维数据集，这些数据集是作为初始状态和少数过程参数的非线性变换而生成的。将这些数据映射到低维流形有助于更好地理解底层过程，并使其优化。在本文中，我们首先表明，离线非线性谱降维方法，例如，Isomap对此类数据失败，主要是由于存在强时间相关性。然后，我们提出了一种新的方法，熵Isomap，来解决这个问题。所提出的方法被成功地应用于描述有机材料的制造过程的大数据。由此产生的低维表示正确地捕捉过程控制变量，允许低维可视化的材料形态演变，并提供关键的见解，以改善过程。
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-theshelf 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.