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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- Partial requirement for: Ph.D., Arizona State University, 2014Note typethesis
- Includes bibliographical references (p. 84-90)Note typebibliography
- Field of study: Computer science