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Feature learning and the discovery of nonlinear variation patterns in high-dimensional data is an important task in many problem domains, such as imaging, streaming data from sensors, and manufacturing. This

Feature learning and the discovery of nonlinear variation patterns in high-dimensional data is an important task in many problem domains, such as imaging, streaming data from sensors, and manufacturing. This dissertation presents several methods for learning and visualizing nonlinear variation in high-dimensional data. First, an automated method for discovering nonlinear variation patterns using deep learning autoencoders is proposed.

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    Date Created
    • 2016
    Resource Type
  • Text
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    • Partial requirement for: Ph.D., Arizona State University, 2016
      Note type
      thesis
    • Includes bibliographical references (pages 120-122)
      Note type
      bibliography
    • Field of study: Industrial engineering

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    by Phillip Howard

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