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Semi-sparse PCA

Eldén, Lars and Trendafilov, Nickolay (2019). Semi-sparse PCA. Psychometrika, 84(1) pp. 164–185.

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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.

Item Type: Journal Item
Copyright Holders: 2018 The Psychometric Society
ISSN: 1860-0980
Keywords: Alternative factor analysis; Matrix decompositions; Least squares; Stiefel manifold; Sparse PCA; Robust PCA
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 57495
Depositing User: Nickolay Trendafilov
Date Deposited: 12 Nov 2018 13:17
Last Modified: 27 Nov 2019 02:15
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