A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection
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An analysis of high dimensional data can offer a detailed description of a system but is often challenged by the curse of dimensionality. General dimensionality reduction techniques can alleviate such difficulty by extracting a few important features, but they are limited due to the lack of interpretability and connectivity to actual decision making associated with each physical variable. Important variable selection techniques, as an alternative, can maintain the interpretability, but they often involve a greedy search that is susceptible to failure in capturing important interactions. This research proposes a new method that produces subspaces, reduced-dimensional physical spaces, based on a randomized search and forms an ensemble of models for critical subspaces. When applied to high-dimensional data collected from a composite metal development process, the proposed method shows its superiority in prediction and important variable selection.
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