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Feature representations for raw data is one of the most important component in a machine learning system. Traditionally, features are \textit{hand crafted} by domain experts which can often be a

Feature representations for raw data is one of the most important component in a machine learning system. Traditionally, features are \textit{hand crafted} by domain experts which can often be a time consuming process. Furthermore, they do not generalize well to unseen data and novel tasks. Recently, there have been many efforts to generate data-driven representations using clustering and sparse models. This dissertation focuses on building data-driven unsupervised models for analyzing raw data and developing efficient feature representations.

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    Date Created
    • 2014
    Resource Type
  • Text
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    • Partial requirement for: Ph.D., Arizona State University, 2014
      Note type
      thesis
    • Includes bibliographical references (p. 124-132)
      Note type
      bibliography
    • Field of study: Electrical engineering

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    by Prasanna S. Sattigeri

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