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Outlier-Aware Applications in High-Dimensional Industrial Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Outlier-Aware Applications in High-Dimensional Industrial Systems./
作者:	Sergin, Nurettin Dorukhan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	121 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Industrial engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28549687
ISBN:	9798535543772

Outlier-Aware Applications in High-Dimensional Industrial Systems.
Sergin, Nurettin Dorukhan.

Outlier-Aware Applications in High-Dimensional Industrial Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 121 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Arizona State University, 2021.

This item must not be sold to any third party vendors.

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).

ISBN: 9798535543772Subjects--Topical Terms:

526216
Industrial engineering.
Subjects--Index Terms:

Deep learning

Outlier-Aware Applications in High-Dimensional Industrial Systems.
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650 4 $a Computer science. $3 523869
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