Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Deterministic annealing EM algorithm
Neural Networks
Faithful representations with topographic maps
Neural Networks
Self-Organizing Maps
A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Information-Theoretic Competitive Learning with Inverse Euclidean Distance Output Units
Neural Processing Letters
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Community self-organizing map and its application to data extraction
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
PolSOM: A new method for multidimensional data visualization
Pattern Recognition
Free energy-based competitive learning for self-organizing maps
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Vector quantization by deterministic annealing
IEEE Transactions on Information Theory
A new approach for data clustering and visualization using self-organizing maps
Expert Systems with Applications: An International Journal
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
A new model of self-organizing neural networks and its application in data projection
IEEE Transactions on Neural Networks
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
Entropy-based kernel mixture modeling for topographic map formation
IEEE Transactions on Neural Networks
PRSOM: a new visualization method by hybridizing multidimensional scaling and self-organizing map
IEEE Transactions on Neural Networks
Quantifying the neighborhood preservation of self-organizing feature maps
IEEE Transactions on Neural Networks
Explicit Magnification Control of Self-Organizing Maps for “Forbidden” Data
IEEE Transactions on Neural Networks
Automatic Cluster Detection in Kohonen's SOM
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Unsupervised Visual Data Mining Using Self-organizing Maps and a Data-driven Color Mapping
IV '12 Proceedings of the 2012 16th International Conference on Information Visualisation
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In this paper, we consider a society of neurons where different types of neurons interact with each other. For the first approximation to this society, we suppose two types of neurons, namely, individually and collectively treated neurons. Just as individuality must be in harmony with collectivity in actual societies, individually treated neurons must cooperate with collectively treated neurons as much as possible. We here realize this cooperation by making individually treated neurons as similar to collectively treated neurons as possible. The difference between individually and collectively treated neurons is represented by the Kullback---Leibler divergence. This divergence is minimized using free energy minimization. We applied the method to three problems from the well-known machine learning database, namely wine and protein classification, and the image segmentation problem. In all three problems, we succeeded in producing clearer class structures than those obtainable using the conventional SOM. However, we observed that the fidelity to input patterns deteriorated. For this problem, we found that careful treatment of learning processes were needed to keep fidelity to input patterns at an acceptable level.