Kernel-based topographic map formation by local density modeling
Neural Computation
Joint entropy maximization in kernel-based topographic maps
Neural Computation
Kernel-based topographic map formation achieved with an information-theoretic approach
Neural Networks - New developments in self-organizing maps
Expanding self-organizing map for data visualization and cluster analysis
Information Sciences: an International Journal - Special issue: Soft computing data mining
Vector quantization using information theoretic concepts
Natural Computing: an international journal
Maximum Likelihood Topographic Map Formation
Neural Computation
Topographic map formation of factorized Edgeworth-expanded kernels
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
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
Mixture density modeling, Kullback-Leibler divergence, and differential log-likelihood
Signal Processing - Special issue: Information theoretic signal processing
Nonlinear Principal Manifolds --- Adaptive Hybrid Learning Approaches
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Decomposition of mixed pixels based on bayesian self-organizing map and Gaussian mixture model
Pattern Recognition Letters
Generalized Self-Organizing Mixture Autoregressive Model
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
A hybrid EM approach to spatial clustering
Computational Statistics & Data Analysis
Probabilistic PCA self-organizing maps
IEEE Transactions on Neural Networks
Multivariate Student-t self-organizing maps
Neural Networks
Generalized Self-Organizing Mixture Autoregressive Model for Modeling Financial Time Series
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Random Projection RBF Nets for Multidimensional Density Estimation
International Journal of Applied Mathematics and Computer Science - Issues in Fault Diagnosis and Fault Tolerant Control
Adaptive nonlinear manifolds and their applications to pattern recognition
Information Sciences: an International Journal
Signal classification with self-organizing mixture networks
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
Local matrix adaptation in topographic neural maps
Neurocomputing
Classification of SAR imagery using multiscale self-organizing network
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
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
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Federated simulation of network performance using packet flow modeling
SCSC '09 Proceedings of the 2009 Summer Computer Simulation Conference
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A self-organizing mixture network (SOMN) is derived for learning arbitrary density functions. The network minimizes the Kullback-Leibler information metric by means of stochastic approximation methods. The density functions are modeled as mixtures of parametric distributions. A mixture needs not to be homogenous, i.e., it can have different density profiles. The first layer of the network is similar to Kohonen's self-organizing map (SOM), but with the parameters of the component densities as the learning weights. The winning mechanism is based on maximum posterior probability, and updating of the weights is limited to a small neighborhood around the winner. The second layer accumulates the responses of these local nodes, weighted by the learned mixing parameters. The network possesses a simple structure and computational form, yet yields fast and robust convergence. The network has a generalization ability due to the relative entropy criterion used. Applications to density profile estimation and pattern classification are presented. The SOMN can also provide an insight to the role of neighborhood function used in the SOM