An Information Theoretic ART for Robust Unsupervised Learning
In this paper, an information-theoretic-based adaptive resonance theory (IT-ART) neural network architecture is presented. Each IT-ART category is defined by the first and second order statistics (mean and covariance matrix) of the cluster or class it represents. This information is used to estimate probability density functions (multivariate Gaussians) and compute the activation functions. The match function of the vigilance check is based on Renyi's quadratic cross-entropy: it is the cross information potential. Experiments involving several real world and synthetic data sets were carried out to assess the performance of IT-ART, which was measured in terms of external validity indices. IT-ART expanded the range of successful vigilance parameter values in these tests.
L. E. Brito Da Silva and D. C. Wunsch, "An Information Theoretic ART for Robust Unsupervised Learning," Proceedings of the International Joint Conference on Neural Networks (2016, Vancouver, Canada), Institute of Electrical and Electronics Engineers (IEEE), Jul 2016.
The definitive version is available at https://doi.org/10.1109/IJCNN.2016.7727583
2016 International Joint Conference on Neural Networks, IJCNN 2016 (2016: Jul. 24-29, Vancouver, Canada)
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
Arts Computing; Covariance Matrix; Information Theory; Network Architecture; Activation Functions; Adaptive Resonance Theory; External Validities; Information Potential; Match Functions; Second Order Statistics; Synthetic Datasets; Vigilance Parameter; Probability Density Function
Article - Conference proceedings
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