Description
This thesis introduces new techniques for clustering distributional data according to their geometric similarities. This work builds upon the optimal transportation (OT) problem that seeks global minimum cost for matching distributional data and leverages the connection between OT and power diagrams to solve different clustering problems. The OT formulation is based on the variational principle to differentiate hard cluster assignments, which was missing in the literature. This thesis shows multiple techniques to regularize and generalize OT to cope with various tasks including clustering, aligning, and interpolating distributional data. It also discusses the connections of the new formulation to other OT and clustering formulations to better understand their gaps and the means to close them. Finally, this thesis demonstrates the advantages of the proposed OT techniques in solving machine learning problems and their downstream applications in computer graphics, computer vision, and image processing.
Details
Title
- Transportation Techniques for Geometric Clustering
Contributors
- Mi, Liang (Author)
- Wang, Yalin (Thesis advisor)
- Chen, Kewei (Committee member)
- Karam, Lina (Committee member)
- Li, Baoxin (Committee member)
- Turaga, Pavan (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2020
Subjects
Resource Type
Collections this item is in
Note
- Doctoral Dissertation Computer Engineering 2020