GTM: the generative topographic mapping
Neural Computation
A stochastic self-organizing map for proximity data
Neural Computation
Self-Organizing Maps
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generative probabilistic approach to visualizing sets of symbolic sequences
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
On the equivalence between kernel self-organising maps and self-organising mixture density networks
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
On the information and representation of non-Euclidean pairwise data
Pattern Recognition
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
Graph-Based Representation of Symbolic Musical Data
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
The Journal of Machine Learning Research
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Topographic mapping of large dissimilarity data sets
Neural Computation
IEEE Transactions on Information Theory
Quantifying the neighborhood preservation of self-organizing feature maps
IEEE Transactions on Neural Networks
Topographic mapping of dissimilarity data
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Linear time heuristics for topographic mapping of dissimilarity data
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Prototype-based classification of dissimilarity data
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Learning relevant time points for time-series data in the life sciences
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
How to quantitatively compare data dissimilarities for unsupervised machine learning?
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Cartogram visualization for nonlinear manifold learning models
Data Mining and Knowledge Discovery
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The generative topographic mapping (GTM) has been proposed as a statistical model to represent high-dimensional data by a distribution induced by a sparse lattice of points in a low-dimensional latent space, such that visualization, compression, and data inspection become possible. The formulation in terms of a generative statistical model has the benefit that relevant parameters of the model can be determined automatically based on an expectation maximization scheme. Further, the model offers a large flexibility such as a direct out-of-sample extension and the possibility to obtain different degrees of granularity of the visualization without the need of additional training. Original GTM is restricted to Euclidean data points in a given Euclidean vector space. Often, data are not explicitly embedded in a Euclidean vector space, rather pairwise dissimilarities of data can be computed, i.e. the relations between data points are given rather than the data vectors themselves. We propose a method which extends the GTM to relational data and which allows us to achieve a sparse representation of data characterized by pairwise dissimilarities, in latent space. The method, relational GTM, is demonstrated on several benchmarks.