Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
On a Certain Class of Algorithms for Noise Removal in Image Processing: A Comparative Study
ITCC '02 Proceedings of the International Conference on Information Technology: Coding and Computing
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
On relative convergence properties of principal component analysis algorithms
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
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Self-organization is one of the most important learning paradigms of neural systems. The purpose of an algorithm for self-organizing learning is to discover significant patterns or features in the input data without the help provided by an external teacher. The ability to adapt to the environment without the provision of an external teacher is encountered in nature in most intelligent organisms. In this paradigm, the lack of teaching signals is compensated for by an inner purpose, i.e., some built-in criterion or objective function that the system seeks to optimize. We investigate the comparative performance of different PCA algorithms derived from Hebbian learning, lateral interaction algorithms and gradient-based learning for digital signal compression and image processing purposes. The final sections of the paper focus on PCA based approaches for image restoration task and on PCA based shrinkage technique for noise removal. The proposed algorithms were tested and some of the results are presented in the final part of each section.