GTM: the generative topographic mapping
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Kalman filter implementation of self-organizing feature maps
Neural Computation
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Model for Probabilistic Principal Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical GTM: Constructing Localized Nonlinear Projection Manifolds in a Principled Way
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Learning and Probabilistic Reasoning
Computational Learning and Probabilistic Reasoning
Self-Organizing Maps
Self-Organizing Map Formation: Foundations of Neural Computation
Self-Organizing Map Formation: Foundations of Neural Computation
Robust mixture modelling using the t distribution
Statistics and Computing
Self-Organizing Dynamic Graphs
Neural Processing Letters
Unsupervised learning in neural computation
Theoretical Computer Science - Natural computing
Kernel-based topographic map formation by local density modeling
Neural Computation
Joint entropy maximization in kernel-based topographic maps
Neural Computation
Gas identification using density models
Pattern Recognition Letters
Maximum Likelihood Topographic Map Formation
Neural Computation
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Learning Nonlinear Image Manifolds by Global Alignment of Local Linear Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
On the equivalence between kernel self-organising maps and self-organising mixture density networks
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Adaptive filtering with the self-organizing map: a performance comparison
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Incremental MLLR speaker adaptation by fuzzy logic control
Pattern Recognition
Expert Systems with Applications: An International Journal
Computational Statistics & Data Analysis
Document analysis and visualization with zero-inflated poisson
Data Mining and Knowledge Discovery
Model-based clustering by probabilistic self-organizing maps
IEEE Transactions on Neural Networks
Probabilistic PCA self-organizing maps
IEEE Transactions on Neural Networks
Acceleration of the EM algorithm via extrapolation methods: Review, comparison and new methods
Computational Statistics & Data Analysis
Multivariate Student-t self-organizing maps
Neural Networks
On EM Estimation for Mixture of Multivariate t-Distributions
Neural Processing Letters
Automatic model selection by cross-validation for probabilistic PCA
Neural Processing Letters
Self-organizing mixture models
Neurocomputing
Statistical modeling of complex backgrounds for foreground object detection
IEEE Transactions on Image Processing
Self-organizing mixture networks for probability density estimation
IEEE Transactions on Neural Networks
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Entropy-based kernel mixture modeling for topographic map formation
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Web content management by self-organization
IEEE Transactions on Neural Networks
PRSOM: a new visualization method by hybridizing multidimensional scaling and self-organizing map
IEEE Transactions on Neural Networks
Generalizing self-organizing map for categorical data
IEEE Transactions on Neural Networks
Visualization of Tree-Structured Data Through Generative Topographic Mapping
IEEE Transactions on Neural Networks
Stochastic approximation for background modelling
Computer Vision and Image Understanding
Reduction of JPEG compression artifacts by kernel regression and probabilistic self-organizing maps
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
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The original self-organizing feature map did not define any probability distribution on the input space. However, the advantages of introducing probabilistic methodologies into self-organizing map models were soon evident. This has led to a wide range of proposals which reflect the current emergence of probabilistic approaches to computational intelligence. The underlying estimation theories behind them derive from two main lines of thought: the expectation maximization methodology and stochastic approximation methods. Here, we present a comprehensive view of the state of the art, with a unifying perspective of the involved theoretical frameworks. In particular, we examine the most commonly used continuous probability distributions, self-organization mechanisms, and learning schemes. Special emphasis is given to the connections among them and their relative advantages depending on the characteristics of the problem at hand. Furthermore, we evaluate their performance in two typical applications of self-organizing maps: classification and visualization.