Elements of information theory
Elements of information theory
GTM: the generative topographic mapping
Neural Computation
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
An information-theoretic analysis of hard and soft assignment methods for clustering
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Mixtures of probabilistic principal component analyzers
Neural Computation
An Introduction to Variational Methods for Graphical Models
Machine Learning
A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Coordinating Principal Component Analyzers
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Heterogeneous Kohonen Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
The Adaptive Subspace Map for Texture Segmentation
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Density Estimation by Mixture Models with Smoothing Priors
Neural Computation
Self-organizing maps, vector quantization, and mixture modeling
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Learning Nonlinear Image Manifolds by Global Alignment of Local Linear Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Self-Organizing Graphs
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Model-based clustering by probabilistic self-organizing maps
IEEE Transactions on Neural Networks
Probabilistic PCA self-organizing maps
IEEE Transactions on Neural Networks
Multivariate Student-t self-organizing maps
Neural Networks
From variable weighting to cluster characterization in topographic unsupervised learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Self-organization of probabilistic PCA models
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Probabilistic self-organizing maps for qualitative data
Neural Networks
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
Self-organizing hidden Markov models
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Simultaneous pattern and variable weighting during topological clustering
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Weighted topological clustering for categorical data
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Robust Bayesian fitting of 3D morphable model
Proceedings of the 10th European Conference on Visual Media Production
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
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We present an expectation-maximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have a normal EM algorithm. Compared to other mixture model approaches to self-organizing maps (SOMs), the function our algorithm maximizes has a clear interpretation: it sums data log-likelihood and a penalty term that enforces self-organization. Our approach allows principled handling of missing data and learning of mixtures of SOMs. We present example applications illustrating our approach for continuous, discrete, and mixed discrete and continuous data.