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政大典藏 > College of Science > Department of Psychology > Periodical Articles > Item 140.119/144834

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/144834

Title:	Accelerating Item Factor Analysis on GPU with Python Package xifa
Authors:	黃柏僩 Huang, Po-Hsien
Contributors:	心理系
Keywords:	Item factor analysis;Item response theory;Deep learning;Parallel computing
Date:	2023-01
Issue Date:	2023-05-29
Abstract:	Item parameter estimation is a crucial step when conducting item factor analysis (IFA). From the view of frequentist estimation, marginal maximum likelihood (MML) seems to be the gold standard. However, fitting a high-dimensional IFA model by MML is still a challenging task. The current study demonstrates that with the help of a GPU (graphics processing unit) and carefully designed vectorization, the computational time of MML could be largely reduced for large-scale IFA applications. In particular, a Python package called xifa (accelerated item factor analysis) is developed, which implements a vectorized Metropolis–Hastings Robbins–Monro (VMHRM) algorithm. Our numerical experiments show that the VMHRM on a GPU may run 33 times faster than its CPU version. When the number of factors is at least five, VMHRM (on GPU) is much faster than the Bock–Aitkin expectation maximization, MHRM implemented by mirt (on CPU), and the importance-weighted autoencoder (on GPU). The GPU-implemented VMHRM is most appropriate for high-dimensional IFA with large data sets. We believe that GPU computing will play a central role in large-scale psychometric modeling in the near future.
Relation:	Behavior Research Methods, Vol.55, pp.4403-4418
Data Type:	article
DOI link:	https://doi.org/10.3758/s13428-022-02024-x
DOI:	10.3758/s13428-022-02024-x
Appears in Collections:	[Department of Psychology] Periodical Articles

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