Pattern Classification Based on a Piecewise Multi-linear Model for the Class Probability Densities

Authors:
Edgard Nyssen;Luc Van Kempen;Hichem Sahli
Affiliations:
-;-;-
Venue:
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Year:
2000

Citing 1
Cited 1

Pattern recognition: statistical, structural and neural approaches

Pattern recognition: statistical, structural and neural approaches

Piecewise Multi-linear PDF Modelling, Using an ML Approach

Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition

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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.