Elements of information theory
Elements of information theory
GTM: the generative topographic mapping
Neural Computation
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
A Combined Latent Class and Trait Model for the Analysis and Visualization of Discrete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Self-Organizing Maps
Variational Extensions to EM and Multinomial PCA
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site
Data Mining and Knowledge Discovery
The Journal of Machine Learning Research
A generative probabilistic approach to visualizing sets of symbolic sequences
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Scalable Generative Topographic Mapping for Sparse Data Sequences
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
ProbMap -- A probabilistic approach for mapping large document collections
Intelligent Data Analysis
Competitive learning algorithms for robust vector quantization
IEEE Transactions on Signal Processing
Independent component analysis using Potts models
IEEE Transactions on Neural Networks
Probabilistic latent semantic visualization: topic model for visualizing documents
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The Block Generative Topographic Mapping
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
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We propose a model-based approach to the twofold problem of prediction and exploratory analysis of heterogeneous symbolic sequence collections. Our model is based on seeking low entropy local representations joined together with a smooth nonlinear mixing process. Low entropy components are desirable, as they tend to be both more interpretable and more predictable. The nonlinear mixing in turn acts as a regulariser, and in addition, it creates a topographic ordering of the sequence histories, which is useful for exploratory purposes. The combination of these two modelling elements is performed through the generative probabilistic formalism, which ensures a flexible and technically sound predictive modelling framework. Unlike previous generative topographic modelling approaches for discrete data, the estimation algorithm associated with our model is designed to scale to large data sets by exploiting data sparseness. In addition, local convergence is guaranteed without the need for tuning optimisation parameters or making approximations to the non-Gaussian likelihood. These characteristics make it the first generative topographic model for discrete symbolic data with large scale real-world applicability. We analyse and discuss the relationship of our approach with a number of models and methods. We empirically demonstrate robustness against varying sample sizes, leading to significant improvements in terms of predictive performance over the state of the art. Finally we detail an application to the prediction and exploratory analysis of a large real-world web navigation sequence collection.