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Using Visualization, Variable Selection and Feature Extraction to Learn from Industrial Data;Doctoral thesis

机译：利用可视化，变量选择和特征提取来学习工业数据;博士论文

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摘要

Although the engineers of industry have access to process data, they seldom use advanced statistical tools to solve process control problems. The author believes the reason for this reluctance is in the history of the development of statistical tools, which were developed in the era of rigorous mathematical modeling, manual computation and small data sets. This created sophisticated tools. The engineers do not understand the requirements of these algorithms related, for example, to pre-processing of data. If algorithms are fed with unsuitable data, or parameterized poorly, they produce unreliable results, which may lead an engineer to turn down statistical analysis in general. This thesis looks for algorithms that probably do not impress the champions of statistics, but serve process engineers. This thesis advocates three properties in an algorithm: supervised operation, robustness and understandability.

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