GTM: the generative topographic mapping
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Self-Organizing Maps
Learing Fine Motion by Using the Hierarchical Extended Kohonen Map
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Mixtures of probabilistic principal component analyzers
Neural Computation
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
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections
Journal of Intelligent Information Systems
Visual exploration of production data using small multiples design with non-uniform color mapping
Computers and Industrial Engineering
Mixture of experts classification using a hierarchical mixture model
Neural Computation
Non-linear Bayesian Image Modelling
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
Boosting Mixture Models for Semi-supervised Learning
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
A General Framework for a Principled Hierarchical Visualization of Multivariate Data
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Discriminatory mining of gene expression microarray data
Journal of VLSI Signal Processing Systems - Special issue on signal processing and neural networks for bioinformatics
Automated hierarchical mixtures of probabilistic principal component analyzers
ICML '04 Proceedings of the twenty-first international conference on Machine learning
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Modeling and on-line recognition of PD signal buried in excessive noise
Signal Processing
Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization
IEEE Transactions on Knowledge and Data Engineering
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Top 10 Unsolved Information Visualization Problems
IEEE Computer Graphics and Applications
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
Hierarchical Feature Extraction for Compact Representation and Classification of Datasets
Neural Information Processing
Multiple Manifolds Learning Framework Based on Hierarchical Mixture Density Model
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Subspace manifold learning with sample weights
Image and Vision Computing
Probabilistic PCA self-organizing maps
IEEE Transactions on Neural Networks
Mode-kn factor analysis for image ensembles
IEEE Transactions on Image Processing
Bayesian extreme components analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Linear Time Model Selection for Mixture of Heterogeneous Components
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Self-organization of probabilistic PCA models
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
On cluster tree for nested and multi-density data clustering
Pattern Recognition
Dimension reduction for model-based clustering
Statistics and Computing
A top-down approach for hierarchical cluster exploration by visualization
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Boosted multi-class semi-supervised learning for human action recognition
Pattern Recognition
A combined fbm and PPCA based signal model for on-line recognition of PD signal
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Identification of piecewise affine systems based on statistical clustering technique
Automatica (Journal of IFAC)
Proceedings of the ACM Symposium on Applied Perception
Cartogram visualization for nonlinear manifold learning models
Data Mining and Knowledge Discovery
Expert Systems with Applications: An International Journal
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Visualization has proven to be a powerful and widely-applicable tool for the analysis and interpretation of multivariate data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space, it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and subclusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach on a toy data set, and we then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multiphase flows in oil pipelines, and to data in 36 dimensions derived from satellite images. A Matlab software implementation of the algorithm is publicly available from the World Wide Web.