Non-linear dimensionality reduction techniques for unsupervised feature extraction
Pattern Recognition Letters
Visualization of transformed multivariate data sets with autoassociative neural networks
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
A Novel Measure for Quantifying the Topology Preservation of Self-Organizing Feature Maps
Neural Processing Letters
Piecewise Linear Projection Based on Self-Organizing Map
Neural Processing Letters
Unsupervised feature extraction using neuro-fuzzy approach
Fuzzy Sets and Systems - Information processing
Distance Matrix Based Clustering of the Self-Organizing Map
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Evolving High-Posterior Self-Organizing Maps
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Semantic Structuring and Visual Querying of Document Abstracts in Digital Libraries
ECDL '98 Proceedings of the Second European Conference on Research and Advanced Technology for Digital Libraries
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Self-organizing maps with recursive neighborhood adaptation
Neural Networks - New developments in self-organizing maps
Data visualisation and manifold mapping using the ViSOM
Neural Networks - New developments in self-organizing maps
Nonlinear feature extraction using a neuro genetic hybrid
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Class distribution on SOM surfaces for feature extraction and object retrieval
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Neural unit shape representation: A new SOM-based visualisation
International Journal of Knowledge-based and Intelligent Engineering Systems
DS '08 Proceedings of the 11th International Conference on Discovery Science
Exploring Topology Preservation of SOMs with a Graph Based Visualization
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Application of Topology Preserving Ensembles for Sensory Assessment in the Food Industry
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Automated Ham Quality Classification Using Ensemble Unsupervised Mapping Models
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Similarity-Based Clustering
A swarm-inspired projection algorithm
Pattern Recognition
RankVisu: Mapping from the neighborhood network
Neurocomputing
A local semi-supervised Sammon algorithm for textual data visualization
Journal of Intelligent Information Systems
Topology Preserving Visualization Methods for Growing Self-Organizing Maps
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
Community self-organizing map and its application to data extraction
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A new approach to clustering data with arbitrary shapes
Pattern Recognition
ViSOM ensembles for visualization and classification
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Visualizing high-dimensional input data with growing self-organizing maps
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Boosting unsupervised competitive learning ensembles
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Graph based representations of density distribution and distances for self-organizing maps
IEEE Transactions on Neural Networks
Gastroenterology dataset clustering using possibilistic Kohonen maps
WSEAS Transactions on Information Science and Applications
Intelligently resolving point occlusion
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Neuro-fuzzy methodology for selecting genes mediating lung cancer
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Improvements on the visualization of clusters in geo-referenced data using Self-Organizing Maps
Computers & Geosciences
Class visualization of high-dimensional data with applications
Computational Statistics & Data Analysis
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
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A nonlinear projection method is presented to visualize high-dimensional data as a 2D image. The proposed method is based on the topology preserving mapping algorithm of Kohonen. The topology preserving mapping algorithm is used to train a 2D network structure. Then the interpoint distances in the feature space between the units in the network are graphically displayed to show the underlying structure of the data. Furthermore, we present and discuss a new method to quantify how well a topology preserving mapping algorithm maps the high-dimensional input data onto the network structure. This is used to compare our projection method with a well-known method of Sammon (1969). Experiments indicate that the performance of the Kohonen projection method is comparable or better than Sammon's method for the purpose of classifying clustered data. Its time-complexity only depends on the resolution of the output image, and not on the size of the dataset. A disadvantage, however, is the large amount of CPU time required