Abstract:Factor analysis is routinely used for dimensionality reduction. However, a major issue is 'brittleness' in which one can obtain substantially different factors in analyzing similar datasets. Factor models have been developed for multi-study data by using additive expansions incorporating common and study-specific factors. However, allowing study-specific factors runs counter to the goal of producing a single set of factors that hold across studies. As an alternative, we propose a class of Perturbed Factor Anal… Show more
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