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Statistical shape analysis techniques commonly employed in the medical imaging community, such as Active Shape Models or Active Appearance Models, rely on Principal Component Analysis (PCA) to decompose shape variability into a reduced set of interpretable components. In this paper we propose Principal Factor Analysis (PFA) as an alternative and complementary tool to PCA providing a decomposition into modes of variation that can be more easily interpretable, while still being a linear efficientdoi:10.1109/isbi.2007.357077 dblp:conf/isbi/ReyesLMANB07 fatcat:f3b2qb2wszgm3pwc6it2a4jske