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Outlier-Aware Applications in High-Dimensional Industrial Systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Outlier-Aware Applications in High-Dimensional Industrial Systems./
Author:	Sergin, Nurettin Dorukhan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	121 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
Subject:	Industrial engineering. -
Online resource:	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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300 $a 121 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Yan, Hao.
502 $a Thesis (Ph.D.)--Arizona State University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a 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).
590 $a School code: 0010.
650 4 $a Industrial engineering. $3 526216
650 4 $a Computer science. $3 523869
650 4 $a Statistics. $3 517247
650 4 $a Artificial intelligence. $3 516317
650 4 $a Subways. $3 1061611
650 4 $a Simulation. $3 644748
650 4 $a Accuracy. $3 3559958
650 4 $a Manufacturing. $3 3389707
650 4 $a Datasets. $3 3541416
650 4 $a Neural networks. $3 677449
650 4 $a Variables. $3 3548259
650 4 $a Decomposition. $3 3561186
650 4 $a Approximation. $3 3560410
650 4 $a Methods. $3 3560391
650 4 $a Clustering. $3 3559215
650 4 $a Case studies. $2 itrt $3 996239
653 $a Deep learning
653 $a Dimensionality reduction
653 $a High dimensionality
653 $a Machine learning
653 $a Outlier analysis
653 $a Spatiotemporal analysis
690 $a 0546
690 $a 0984
690 $a 0463
690 $a 0800
710 2 $a Arizona State University. $b Industrial Engineering. $3 2098642
773 0 $t Dissertations Abstracts International $g 83-03B.
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791 $a Ph.D.
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