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


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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