A Generalized Autoassociator Model for Face Processing and Sex Categorization: from Principal Components to Multivariate Analysis

A generalized autoassociator model for face processing and sex categorization: From principal components to multivariate analysis. ABSTRACT In this paper we propose a generalized version of the classical linear au-toassociator that can be shown to implement a generalized least-squares approximation under linear constraints. The standard linear autoassociator is known to implement principal component analysis, whereas the generalized model implements the general linear model (e.g., canonical correlation). In practical terms, this generalization allows for the imposition of a priori constraints that enable diierential weighting of both individual units of the input code and individual stimuli. As an illustration of the utility of the generalized model, we present simulations comparing the accuracy and learning speed of the standard and generalized versions of the autoasso-ciator for the problem of categorizing faces by sex. We show that while the two models are equally accurate, the generalized model learns the task considerably faster than does the standard model.