Graduation Semester and Year
Spring 2024
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science and Engineering
First Advisor
William J. Beksi
Second Advisor
Keaton Hamm
Abstract
In this article, we hope to represent the current state of the art of manifold learning in an understandable and approachable way. The authors will present a general overview core algorithms associated with linear and nonlinear dimensionality reduction techniques, give rudimentary definitions from differential geometry, and tenets of robotic perception, manipulation and path planning. Some of the historical applications of these algorithms will be presented, as well as conjectures about future uses, through examples from peer-reviewed journals.
Keywords
Manifold, NLDR, Manifold learning
Disciplines
Data Science | Geometry and Topology | Other Mathematics | Robotics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
hawkins, marcus, "Manifold Learning in Robotics: A Tutorial and Survey" (2024). Computer Science and Engineering Theses. 3.
https://mavmatrix.uta.edu/cse_theses/3
Included in
Data Science Commons, Geometry and Topology Commons, Other Mathematics Commons, Robotics Commons