Efficient modeling of the inter-individual variations of head-related transfer functions (HRTFs) is a key matterto the individualization of binaural synthesis. In previous work, we augmented a dataset of 119 pairs of earshapes and pinna-related transfer functions (PRTFs), thus creating a wide dataset of 1005 ear shapes and PRTFsgenerated by random ear drawings (WiDESPREaD) and acoustical simulations. In this article, we investigate thedimensionality reduction capacity of two principal component analysis (PCA) models of magnitude PRTFs, trainedon WiDESPREaD and on the original dataset, respectively. We find that the model trained on the WiDESPREaDdataset performs best, regardless of the number of retained principal components.