DocumentCode
2199167
Title
Kernel-based topographic map formation achieved with normalized Gaussian competition
Author
Van Hulle, Marc M.
Author_Institution
Laboratorium voor Neuro- en Psychofysiologie, Katholieke Univ., Leuven, Belgium
fYear
2002
fDate
2002
Firstpage
169
Lastpage
178
Abstract
A new learning algorithm for kernel-based topographic map formation is introduced. The kernels are Gaussians, and their centers and ranges individually adapted so as to yield an equiprobabilistic topographic map. The converged map also generates a heteroscedastic Gaussian mixture model of the input density. This is verified for both synthetic and real-world examples, and compared with other algorithms for kernel-based topographic map formation.
Keywords
Gaussian distribution; neural nets; unsupervised learning; converged map; equiprobabilistic topographic map; heteroscedastic Gaussian mixture model; input density; kernel-based topographic map formation; learning algorithm; neural network; normalized Gaussian competition; unsupervised competitive learning; Clustering algorithms; Entropy; Kernel; Laboratories; Lattices; Marine vehicles; Maximum likelihood estimation; Neural networks; Neurons; Psychology;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on
Print_ISBN
0-7803-7616-1
Type
conf
DOI
10.1109/NNSP.2002.1030028
Filename
1030028
Link To Document