"Pattern Classiffcation Based on a Piecewise Multi-Linear Model for the Class Probability Densities

When a Bayesian classifier is designed, a model for the class probability density functions (PDFs) has to be chosen. This choice is determined by a trade-off between robustness and low complexity -- which is usually satisfied by simple parametric models, based on a restricted number of parameters -- and the model's ability to fit a large class of PDFs -- which usually requires a high number of model parameters. In this paper, a model is introduced, where the class PDFs are approximated as piecewise multi-linear functions (a generalisation of bilinear functions for an arbitrary dimensionality). This model is compared with classical parametric and non-parametric models, from a point of view of versatility, robustness and complexity. The results of classification and PDF estimation experiments are discussed.