Full metadata
Title
Outlier-Aware Applications in High-Dimensional Industrial Systems
Description
High-dimensional data is omnipresent in modern industrial systems. An imaging sensor in a manufacturing plant a can take images of millions of pixels or a sensor may collect months of data at very granular time steps. Dimensionality reduction techniques are commonly used for dealing with such data. In addition, outliers typically exist in such data, which may be of direct or indirect interest given the nature of the problem that is being solved. Current research does not address the interdependent nature of dimensionality reduction and outliers. Some works ignore the existence of outliers altogether—which discredits the robustness of these methods in real life—while others provide suboptimal, often band-aid solutions. In this dissertation, I propose novel methods to achieve outlier-awareness in various dimensionality reduction methods. The problem is considered from many different angles depend- ing on the dimensionality reduction technique used (e.g., deep autoencoder, tensors), the nature of the application (e.g., manufacturing, transportation) and the outlier structure (e.g., sparse point anomalies, novelties).
Date Created
2021
Contributors
- Sergin, Nurettin Dorukhan (Author)
- Yan, Hao (Thesis advisor)
- Li, Jing (Committee member)
- Wu, Teresa (Committee member)
- Tsung, Fugee (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
120 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.161801
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: Ph.D., Arizona State University, 2021
Field of study: Industrial Engineering
System Created
- 2021-11-16 04:08:47
System Modified
- 2021-11-30 12:51:28
- 2 years 11 months ago
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