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.

DOI: https://doi.org/10.1002/sam.11393

Abstract

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.

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About

• Item ORO ID
• 56319
• Item Type
• Journal Item
• ISSN
• 1932-1872
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  Sparse factor analysis with application to large data sets	RPG--2013--211	The Leverhulme Trust

• Keywords
• alternating iterations; Procrustes problems; rank inequalities
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2018 Wiley Periodicals, Inc.
• Depositing User
• Nickolay Trendafilov