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
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
A generative probabilistic approach to visualizing sets of symbolic sequences
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Semisupervised Learning of Hierarchical Latent Trait Models for Data Visualization
IEEE Transactions on Knowledge and Data Engineering
Computational methods for hidden Markov tree models-an application to wavelet trees
IEEE Transactions on Signal Processing
Visualization of Structured Data via Generative Probabilistic Modeling
Similarity-Based Clustering
Autoencoding ground motion data for visualisation
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
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Recently, generative probabilistic modeling principles were extended to visualization of structured data types, such as sequences. The models are formulated as constrained mixtures of sequence models - a generalization of density-based visualization methods previously developed for static data sets. In order to effectively explore visualization plots, one needs to understand local directional magnification factors, i.e. the extend to which small positional changes on visualization plot lead to changes in local noise models explaining the structured data. Magnification factors are useful for highlighting boundaries between data clusters. In this paper we present two techniques for estimating local metric induced on the sequence space by themodel formulation. We first verify our approach in two controlled experiments involving artificially generated sequences. We then illustrate our methodology on sequences representing chorals by J.S. Bach.