Incremental Eigenanalysis for Classification

Eigenspace models are a convenientway to represent sets of observations with widespread applications, including classi#cation. In this paper we describe a new constructive method for incrementally adding observations to an eigenspace model. Our contribution is to explicitly account forachange in origin as well as a change in the number of eigenvectors needed in the basis set. No other method wehave seen considers change of origin, yet both are needed if an eigenspace model is to be used for classi#cation purposes. We empirically compare our incremental method with two alternatives from the literature and show our method is the more useful for classi#cation because it computes the smaller eigenspace model representing the observations. 1 Introduction The contribution of this paper is a method for incrementally computing eigenspace models in the context of using them for classi#cation. Eigenspace models are widely used in computer vision. Applications include: face recognition #8# where...