Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Selection of Non-zero Loadings in Sparse Principal Component Analysis
Department of Chemical Engineering, University of California, Davis, CA.
Luleå tekniska universitet, Institutionen för ekonomi, teknik och samhälle, Industriell Ekonomi.
Department of Chemical Engineering, University of California, Davis, CA.
Antal upphovsmän: 3
2017 (Engelska)Ingår i: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 162, 160-171 s.Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Principal component analysis (PCA) is a widely accepted procedure for summarizing data through dimensional reduction. In PCA, the selection of the appropriate number of components and the interpretation of those components have been the key challenging features. Sparse principal component analysis (SPCA) is a relatively recent technique proposed for producing principal components with sparse loadings via the variance-sparsity trade-off. Although several techniques for deriving sparse loadings have been offered, no detailed guidelines for choosing the penalty parameters to obtain a desired level of sparsity are provided. In this paper, we propose the use of a genetic algorithm (GA) to select the number of non-zero loadings (NNZL) in each principal component while using SPCA. The proposed approach considerably improves the interpretability of principal components and addresses the difficulty in the selection of NNZL in SPCA. Furthermore, we compare the performance of PCA and SPCA in uncovering the underlying latent structure of the data. The key features of the methodology are assessed through a synthetic example, pitprops data and a comparative study of the benchmark Tennessee Eastman process.

Ort, förlag, år, upplaga, sidor
2017. Vol. 162, 160-171 s.
Nationell ämneskategori
Tillförlitlighets- och kvalitetsteknik
Forskningsämne
Kvalitetsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-61741DOI: 10.1016/j.chemolab.2017.01.018ISI: 000395843700017Scopus ID: 2-s2.0-85012293100OAI: oai:DiVA.org:ltu-61741DiVA: diva2:1070159
Anmärkning

Validerad; 2017; Nivå 2; 2017-02-23 (andbra)

Tillgänglig från: 2017-01-31 Skapad: 2017-01-31 Senast uppdaterad: 2017-11-29Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas

Övriga länkar

Förlagets fulltextScopus

Sök vidare i DiVA

Av författaren/redaktören
Kulahci, Murat
Av organisationen
Industriell Ekonomi
I samma tidskrift
Chemometrics and Intelligent Laboratory Systems
Tillförlitlighets- och kvalitetsteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 184 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf