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Semi-sparse PCA
Linköping University, Department of Mathematics, Computational Mathematics. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-2281-856X
Open Univ, England.
2019 (English)In: Psychometrika, ISSN 0033-3123, E-ISSN 1860-0980, Vol. 84, no 1, p. 164-185Article in journal (Refereed) Published
Abstract [en]

It is well known that the classical exploratory factor analysis (EFA) of data with more observations than variables has several types of indeterminacy. We study the factor indeterminacy and show some new aspects of this problem by considering EFA as a specific data matrix decomposition. We adopt a new approach to the EFA estimation and achieve a new characterization of the factor indeterminacy problem. A new alternative model is proposed, which gives determinate factors and can be seen as a semi-sparse principal component analysis (PCA). An alternating algorithm is developed, where in each step a Procrustes problem is solved. It is demonstrated that the new model/algorithm can act as a specific sparse PCA and as a low-rank-plus-sparse matrix decomposition. Numerical examples with several large data sets illustrate the versatility of the new model, and the performance and behaviour of its algorithmic implementation.

Place, publisher, year, edition, pages
SPRINGER , 2019. Vol. 84, no 1, p. 164-185
Keywords [en]
alternative factor analysis; matrix decompositions; least squares; Stiefel manifold; sparse PCA; robust PCA
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-154834DOI: 10.1007/s11336-018-9650-9ISI: 000458464200009PubMedID: 30483924OAI: oai:DiVA.org:liu-154834DiVA, id: diva2:1294596
Available from: 2019-03-07 Created: 2019-03-07 Last updated: 2019-06-28

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  • apa
  • harvard1
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  • de-DE
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