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.<p>Nonlinear methods would require much more data, unfortunately.