Principal Component Analysis (PCA) is an example of a successful unsupervised statistical
dimensionality reduction method, especially in small sample size problems. Despite
the well-known attractive properties of PCA, the traditional approach does not incorporate
prior information extracted from a specific domain knowledge. The development
of techniques that bring together dimensionality reduction and prior knowledge can be
performed in the framework of supervised learning methods, like Fisher Discriminant
Analysis. Semi-supervised methods can also be applied if only a small number of labeled
samples is available. In this paper, we propose a simple and efficient supervised method
that allows PCA to incorporate explicitly domain knowledge and generates an embedding
space that inherits its optimality properties for dimensionality reduction. The method
relies on discriminant weights given by separating hyperplanes to generate the spatially
weighted PCA. Several experiments using 2D frontal face images and different data sets
have been carried out to illustrate the usefulness of the method for dimensionality reduction,
classification and interpretation of face images.