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Sparsest factor analysis for clustering variables: a matrix decomposition approach

Adachi, Kohei and Trendafilov, Nickolay T. (2018). Sparsest factor analysis for clustering variables: a matrix decomposition approach. Advances in Data Analysis and Classification, 12(3) pp. 559–585.

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We propose a new procedure for sparse factor analysis (FA) such that each variable loads only one common factor. Thus, the loading matrix has a single nonzero element in each row and zeros elsewhere. Such a loading matrix is the sparsest possible for certain number of variables and common factors. For this reason, the proposed method is named sparsest FA (SSFA). It may also be called FA-based variable clustering, since the variables loading the same common factor can be classified into a cluster. In SSFA, all model parts of FA (common factors, their correlations, loadings, unique factors, and unique variances) are treated as fixed unknown parameter matrices and their least squares function is minimized through specific data matrix decomposition. A useful feature of the algorithm is that the matrix of common factor scores is re-parameterized using QR decomposition in order to efficiently estimate factor correlations. A simulation study shows that the proposed procedure can exactly identify the true sparsest models. Real data examples demonstrate the usefulness of the variable clustering performed by SSFA.

Item Type: Journal Item
Copyright Holders: 2017 Springer-Verlag
ISSN: 1862-5355
Keywords: exploratory factor analysis; sparsest loadings; matrix decomposition factor analysis; variable clustering; QR re-parameterization
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 49278
Depositing User: Nickolay Trendafilov
Date Deposited: 02 May 2017 15:25
Last Modified: 21 Jul 2019 04:52
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