Nonparametric Regression Modeling with EquiprobableTopographic Maps and Projection Pursuit Learning with Application to PET Image Processing

Authors:
Marc M. Van Hulle
Affiliations:
K.U. Leuven, Laboratorium voor Neuro- en Psychofysiologie, Campus Gasthuisberg, Herestraat 49, B-3000 Leuven, Belgium
Venue:
Journal of VLSI Signal Processing Systems - special issue on applications of neural networks in biomedical image processing
Year:
1998

Citing 9
Cited 0

On the stationary state of Kohonen's self-organizing sensory mapping

Biological Cybernetics
Digital image processing (2nd ed.)

Digital image processing (2nd ed.)
Introduction to the theory of neural computation

Introduction to the theory of neural computation
Constrained topological mapping for nonparametric regression analysis

Neural Networks
Generalized boundary adaptation rule for minimizing rth power law distortion in high resolution quantization

Neural Networks
Topology-preserving map formation achieved with a purely local unsupervised competitive learning rule

Neural Networks
Self-organizing maps

Self-organizing maps
Neural Computation and Self-Organizing Maps; An Introduction

Neural Computation and Self-Organizing Maps; An Introduction
Topographic map formation by maximizing unconditional entropy: a plausible strategy for “online” unsupervised competitive learning and nonparametric density estimation

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

A recently introduced rule for equiprobable topographicmap formation, called the Vectorial Boundary Adaptation Rule (VBAR), isextended and applied to nonparametric projection pursuit regression. Theperformance of the regression procedure is compared to that of anumber of other nonparametric regression procedures. The procedure isapplied to positron emission tomography (PET) images for adaptivefiltering and data compression purposes.