Using Principal Component Analysis on Images
PCA typically implies that the high dimensional data (images) lie on a low dimensional linear manifold (the principal components). In this case the data of interest (images of people in dresses) likely lies in a very non-linear manifold, so non-linear methods would likely result in much more appropriate models. This is part of why deep learning and tensor methods work so well on image classification tasks.Nonlinear methods would require much more data, unfortunately.