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Advances in data collection technologies have made it cost-effective to obtain heterogeneous data from multiple data sources. Very often, the data are of very high dimension and feature selection is

Advances in data collection technologies have made it cost-effective to obtain heterogeneous data from multiple data sources. Very often, the data are of very high dimension and feature selection is preferred in order to reduce noise, save computational cost and learn interpretable models. Due to the multi-modality nature of heterogeneous data, it is interesting to design efficient machine learning models that are capable of performing variable selection and feature group (data source) selection simultaneously (a.k.a bi-level selection).

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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. 84-90)
      Note type
      bibliography
    • Field of study: Computer science

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    by Shuo Xiang

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