Competitive learning algorithms for vector quantization
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Advances in neural information processing systems 2
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Information Theoretic Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Information-Theoretic Competitive Learning with Inverse Euclidean Distance Output Units
Neural Processing Letters
Lower and Upper Bounds for Misclassification Probability Based on Renyi's Information
Journal of VLSI Signal Processing Systems
Vector quantization using information theoretic concepts
Natural Computing: an international journal
2005 Special Issue: Unifying cost and information in information-theoretic competitive learning
Neural Networks - 2005 Special issue: IJCNN 2005
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Cooperative information maximization with Gaussian activation functions for self-organizing maps
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In this paper, we propose a new information-theoretic method called enhancement and relaxation to discover main features in input patterns. We have so far shown that competitive learning is a process of mutual information maximization between input patterns and connection weights. However, because mutual information is an average over all input patterns and competitive units, it is not adequate for discovering detailed information on the roles of elements in a network. To extract information on the roles of elements in a networks, we enhance or relax competitive units through the elements. Mutual information should be changed by these processes. The change in information is called enhanced information. The enhanced information can be used to discover features in input patterns, because the information includes detailed information on elements in a network. We applied the method to the symmetry data, the well-known Iris problem and the OECD countries classification. In all cases, we succeeded in extracting the main features in input patterns.