The data explosion in the past decade is in part due to the widespread use of rich sensors that measure various physical phenomenon -- gyroscopes that measure orientation in phones and fitness devices, the Microsoft Kinect which measures depth information, etc. A typical application requires inferring the underlying physical phenomenon from data, which is done using machine learning. A fundamental assumption in training models is that the data is Euclidean, i.e. the metric is the standard Euclidean distance governed by the L-2 norm.
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- Partial requirement for: Ph.D., Arizona State University, 2016Note typethesis
- Includes bibliographical references (pages 107-117)Note typebibliography
- Field of study: Electrical engineering