Multilayer feedforward networks are universal approximators
Neural Networks
Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Self-organizing maps
A fast fixed-point algorithm for independent component analysis
Neural Computation
GTM: the generative topographic mapping
Neural Computation
Unsupervised learning
A unifying review of linear Gaussian models
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks and Natural Intelligence
Neural Networks and Natural Intelligence
Kohonen Maps
Neural Computation and Self-Organizing Maps; An Introduction
Neural Computation and Self-Organizing Maps; An Introduction
Nonlinear Autoassociation Is Not Equivalent to PCA
Neural Computation
Temporal BYY learning for state space approach, hidden Markovmodel, and blind source separation
IEEE Transactions on Signal Processing
A class of neural networks for independent component analysis
IEEE Transactions on Neural Networks
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Learning in linear neural networks: a survey
IEEE Transactions on Neural Networks
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
Learning image transformations without training examples
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Let a biogeography-based optimizer train your Multi-Layer Perceptron
Information Sciences: an International Journal
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In this article, we consider unsupervised learning from the point of view of applying neural computation on signal and data analysis problems. The article is an introductory survey, concentrating on the main principles and categories of unsupervised learning. In neural computation, there are two classical categories for unsupervised learning methods and models: first, extensions of principal component analysis and factor analysis, and second, learning vector coding or clustering methods that are based on competitive learning. These are covered in this article. The more recent trend in unsupervised learning is to consider this problem in the framework of probabilistic generative models. If it is possible to build and estimate a model that explains the data in terms of some latent variables, key insights may be obtained into the true nature and structure of the data. This approach is also briefly reviewed.