Self-organizing maps
Neural maps and topographic vector quantization
Neural Networks
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Fuzzy classification by fuzzy labeled neural gas
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Adaptive relevance matrices in learning vector quantization
Neural Computation
Median fuzzy c-means for clustering dissimilarity data
Neurocomputing
Fuzzy labeled self-organizing map for classification of spectra
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Generalized derivative based kernelized learning vector quantization
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Divergence-based vector quantization
Neural Computation
Fuzzy labeled self-organizing map with label-adjusted prototypes
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Self organizing maps for visualization of categories
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Self-Organising maps for classification with metropolis-hastings algorithm for supervision
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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In this paper we propose a new approach to combine unsupervised and supervised vector quantization for clustering and fuzzy classification using the framework of neural vector quantizers like self-organizing maps or neural gas. For this purpose the original cost functions are modified in such a way that both aspects, unsupervised vector quantization and supervised classification, are incorporated. The theoretical justification of the convergence of the new algorithm is given by an adequate redefinition of the underlying dissimilarity measure now interpreted as a dissimilarity in the data space combined with the class label space. This allows a gradient descent learning as known for the original algorithms. Thus a semi-supervised learning scheme is achieved. We apply this method for a spectra image cube of remote sensing data for landtype classification. The obtained fuzzy class visualizations allow a better understanding and interpretation of the spectra.