Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Keeping the neural networks simple by minimizing the description length of the weights
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Faithful representations with topographic maps
Neural Networks
Information Theoretic Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
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
A model of active visual search with object-based attention guiding scan paths
Neural Networks - 2004 Special issue Vision and brain
2005 Special Issue: Unifying cost and information in information-theoretic competitive learning
Neural Networks - 2005 Special issue: IJCNN 2005
2006 Special Issue: Attention as a controller
Neural Networks
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Neural Computation
Feature Discovery by Enhancement and Relaxation of Competitive Units
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
Feature selection in MLPs and SVMs based on maximum output information
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
A novel feature selection method based on normalized mutual information
Applied Intelligence
Image retrieval based on augmented relational graph representation
Applied Intelligence
| Hi-index | 0.00 |
In this paper, we propose structural enhanced information for detecting and visualizing main features in input patterns. We have so far proposed information enhancement for feature detection, where, if we want to focus upon components such as units and connection weights and interpret the functions of the components, we have only to enhance competitive units with the components. Though this information enhancement has given favorable results in feature detection, we further refine the information enhancement and propose structural enhanced information. In structural enhanced information, three types of enhanced information can be differentiated, that is, first-, second- and third-order enhanced information. The first-order information is related to the enhancement of competitive units themselves in a competitive network, and the second-order information is dependent upon the enhancement of competitive units with input patterns. Then, the third-order information is obtained by subtracting the effect of the first-order information from the second-order information. Thus, the third-order information more explicitly represents information on input patterns. With this structural enhanced information, we can estimate more detailed features in input patterns. For demonstrating explicitly and intuitively the improved performance of our method, the conventional SOM was used, and we transformed competitive unit outputs so as to improve visualization. The method was applied to the well-known Iris problem, an OECD countries classification problem and the Johns Hopkins University Ionosphere database. In all these problems, we succeeded in visualizing the detailed and important features of input patterns by using the third-order information.