Three methods for the efficient downdating, composition and splitting of low rank singular value decompositions are proposed.
They are formulated in a closed form, considering the mean information and providing exact results. Although these methods
are presented in the context of computer vision, they can be used in any field forgetting information, combining different
eigenspaces in one or ignoring particular dimensions of the column space of the data. Application examples on face subspace
learning and latent semantic analysis are given and performance results are provided.