Dimensionality reduction with image data

Abstract

A common objective in image analysis is dimensionality reduction. The most often used data-exploratory technique with this objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a Procrustes rotation and show that it leads to a better reconstruction of images. © Springer-Verlag Berlin Heidelberg 2004.