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Exploratory factor analysis of large data matrices

Trendafilov, Nickolay T. and Fontanella, Sara (2019). Exploratory factor analysis of large data matrices. Statistical Analysis and Data Mining, 12(1) pp. 5–11.

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Nowadays, the most interesting applications have data with many more variables than observations and require dimension reduction. With such data, standard exploratory factor analysis (EFA) cannot be applied. Recently, a generalized EFA (GEFA) model was proposed to deal with any type of data: both vertical data(fewer variables than observations) and horizontal data (more variables than observations). The associated algorithm, GEFALS, is very efficient, but still cannot handle data with thousands of variables. The present work modifies GEFALS and proposes a new very fast version, GEFAN. This is achieved by aligning the dimensions of the parameter matrices to their ranks, thus, avoiding redundant calculations. The GEFALS and GEFAN algorithms are compared numerically with well-known data.

Item Type: Journal Item
Copyright Holders: 2018 Wiley Periodicals, Inc.
ISSN: 1932-1872
Project Funding Details:
Funded Project NameProject IDFunding Body
Sparse factor analysis with application to large data setsRPG--2013--211The Leverhulme Trust
Keywords: alternating iterations; Procrustes problems; rank inequalities
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 56319
Depositing User: Nickolay Trendafilov
Date Deposited: 28 Aug 2018 09:05
Last Modified: 15 May 2019 19:57
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