Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Self-organizing maps
A Hierarchical Latent Variable Model for Data Visualization
IEEE Transactions on Pattern Analysis and Machine Intelligence
GTM: the generative topographic mapping
Neural Computation
A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
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
MML clustering of multi-state, Poisson, vonMises circular and Gaussian distributions
Statistics and Computing
A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
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
Recursive Pattern: A Technique for Visualizing Very Large Amounts of Data
VIS '95 Proceedings of the 6th conference on Visualization '95
Exploring N-dimensional databases
VIS '90 Proceedings of the 1st conference on Visualization '90
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Visualizing changes in the structure of data for exploratory feature selection
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A two-way visualization method for clustered data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Visual data mining using principled projection algorithms and information visualization techniques
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Metric properties of structured data visualizations through generative probabilistic modeling
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Recently, we have developed the hierarchical Generative Topographic Mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. In this paper, we propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM). This enables us to visualize data of inherently discrete nature, e.g., collections of documents, in a hierarchical manner. 2) We give the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode, the user selects "regions of interest,驴 whereas in the automatic mode, an unsupervised minimum message length (MML)-inspired construction of a mixture of LTMs is employed. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. 3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualization plots, since they can highlight the boundaries between data clusters. We illustrate our approach on a toy example and evaluate it on three more complex real data sets.