A Topological Approach to Understanding Location-Based Data
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Authors
McAbee, Carson C.
Wakefield, Max D.
Roth, John D.
Scrofani, James W.
Subjects
Machine Learning
Topological Data Analysis
Topological Data Analysis
Advisors
Date of Issue
2018
Date
Publisher
IEEE
Language
Abstract
Location-based services have seen a boon in data production recently which has simultaneously stoked the research community to better understand this type of information. Tra- ditional methods in analyzing such data require significant a priori understanding of the organization of the data. We submit that the nascent field of topological data analysis (TDA) may be able to contribute new insights to analysis of such data without the aforementioned requirement. To this end, we propose two novel methods of embedding such data in order to leverage the expressive power of TDA. To demonstrate its effectiveness we apply the embeddings to maritime automated information system data.
Type
Article
Description
Asilomar 2018
Series/Report No
Department
Computer Science (CS)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
5 p.
Citation
McAbee, Carson, et al. "A Topological Approach to Understanding Location-Based Data." 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.