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Identifying Deviating Systems with Unsupervised Learning
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE).
2008 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

We present a technique to identify deviating systems among a group of systems in a

self-organized way. A compressed representation of each system is used to compute similarity measures, which are combined in an affinity matrix of all systems. Deviation detection and clustering is then used to identify deviating systems based on this affinity matrix.

The compressed representation is computed with Principal Component Analysis and

Kernel Principal Component Analysis. The similarity measure between two compressed

representations is based on the angle between the spaces spanned by the principal

components, but other methods of calculating a similarity measure are suggested as

well. The subsequent deviation detection is carried out by computing the probability of

each system to be observed given all the other systems. Clustering of the systems is

done with hierarchical clustering and spectral clustering. The whole technique is demonstrated on four data sets of mechanical systems, two of a simulated cooling system and two of human gait. The results show its applicability on these mechanical systems.

Place, publisher, year, edition, pages
Högskolan i Halmstad/Sektionen för Informationsvetenskap, Data- och Elektroteknik (IDE) , 2008.
Keywords [en]
Deviation Detection, Clustering, Eigen-Subspace, Machine Learning
Identifiers
URN: urn:nbn:se:hh:diva-1146Local ID: 2082/1525OAI: oai:DiVA.org:hh-1146DiVA, id: diva2:238364
Uppsok
Technology
Available from: 2008-02-25 Created: 2008-02-25 Last updated: 2008-02-25

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf