Growing multi-dimensional self-organizing maps for motion detection
Self-Organizing neural networks
Extensions and modifications of the Kohenen-SOM and applications in remote sensing image analysis
Self-Organizing neural networks
Data Mining and Knowledge Discovery in Medical Applications Using Self-Organizing Maps
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
Speech Dimensionality Analysis on Hypercubical Self-Organizing Maps
Neural Processing Letters
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Organizing and visualizing software repositories using the growing hierarchical self-organizing map
Proceedings of the 2005 ACM symposium on Applied computing
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Image fusion based on topographic mappings using the hyperbolic space
Information Visualization
Hierarchical PCA Using Tree-SOM for the Identification of Bacteria
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Fusion of Topology Preserving Neural Networks
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Unsupervised recursive sequence processing
Neurocomputing
Fuzzy labeled self-organizing map for classification of spectra
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Topology representing network map: a new tool for visualization of high-dimensional data
Transactions on computational science I
Self-organizing incremental neural network and its application
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Self-organizing multilayer perceptron
IEEE Transactions on Neural Networks
Pattern Recognition Letters
Flexible architecture of self organizing maps for changing environments
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Perspectives of self-adapted self-organizing clustering in organic computing
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
Cluster analysis of cortical pyramidal neurons using SOM
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
K-dynamical self organizing maps
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Growing Self-Organizing Map with cross insert for mixed-type data clustering
Applied Soft Computing
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Neural maps project data from an input space onto a neuron position in a (often lower dimensional) output space grid in a neighborhood preserving way, with neighboring neurons in the output space responding to neighboring data points in the input space. A map-learning algorithm can achieve an optimal neighborhood preservation only, if the output space topology roughly matches the effective structure of the data in the input space. We here present a growth algorithm, called the GSOM or growing self-organizing map, which enhances a widespread map self-organization process, Kohonen's self-organizing feature map (SOFM), by an adaptation of the output space grid during learning. The GSOM restricts the output space structure to the shape of a general hypercubical shape, with the overall dimensionality of the grid and its extensions along the different directions being subject of the adaptation. This constraint meets the demands of many larger information processing systems, of which the neural map can be a part. We apply our GSOM-algorithm to three examples, two of which involve real world data. Using recently developed methods for measuring the degree of neighborhood preservation in neural maps, we find the GSOM-algorithm to produce maps which preserve neighborhoods in a nearly optimal fashion