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Pattern Classification Based on a Piecewise Multi-linear Model for the Class Probability Densities
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Pattern Classification Based on a Piecewise Multi-linear Model for the Class Probability Densities
Edgard Nyssen8  , Luc Van Kempen8 and Hichem Sahli8
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Vakgroep Elektronica en Informatieverwerking (ETRO), Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium |
Abstract
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.
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