文章基本信息

标题：A penalized maximum likelihood approach to sparse factor analysis
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作者：Jang Choi ; Gary Oehlert ; Hui Zou 等
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2010
卷号：3
期号：4
页码：429-436
DOI：10.4310/SII.2010.v3.n4.a1
出版社：International Press
摘要：Factor analysis is a popular multivariate analysis method which is used to describe observed variables as linear combinations of hidden factors. In applications one usually needs to rotate the estimated factor loading matrix in order to obtain a more understandable model. In this article, an $\ell_1$ penalization method is introduced for performing sparse factor analysis in which factor loadings naturally adopt a sparse representation, greatly facilitating the interpretation of the fitted factor model. A generalized expectation–maximization algorithm is developed for computing the $\ell_1$ penalized estimator. Efficacy of the proposed methodology and algorithm is demonstrated by simulated and real data.
关键词：adaptive lasso; EM algorithm; factor analysis; lasso; sparse factor loadings