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Statistical and Dynamical Modeling of Riemannian Trajectories with Application to Human Movement Analysis

Abstract 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. However in many cases this assumption is violated, when the data lies on non Euclidean spaces such as Riemannian manifolds. While the underlying geometry accounts for the non-linearity, accurate analysis of huma... (more)
Created Date 2016
Contributor Anirudh, Rushil (Author) / Turaga, Pavan (Advisor) / Cochran, Douglas (Committee member) / Runger, George (Committee member) / Taylor, Thomas (Committee member) / Arizona State University (Publisher)
Subject Computer science / Mathematics / Electrical engineering / activity recognition / dimensionality reduction / human movement analysis / machine learning / manifolds / riemannian geometry
Type Doctoral Dissertation
Extent 127 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Electrical Engineering 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis