GTM: the generative topographic mapping
Neural Computation
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Soft learning vector quantization
Neural Computation
An introduction to variable and feature selection
The Journal of Machine Learning Research
Improved learning of Riemannian metrics for exploratory analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Fuzzy classification by fuzzy labeled neural gas
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
Kernel clustering-based discriminant analysis
Pattern Recognition
Parametric Embedding for Class Visualization
Neural Computation
Principal Manifolds for Data Visualization and Dimension Reduction
Principal Manifolds for Data Visualization and Dimension Reduction
Nonlinear Dimensionality Reduction
Nonlinear Dimensionality Reduction
An introduction to nonlinear dimensionality reduction by maximum variance unfolding
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Nonlinear Dimension Reduction and Visualization of Labeled Data
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Distance learning in discriminative vector quantization
Neural Computation
Adaptive relevance matrices in learning vector quantization
Neural Computation
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
The Journal of Machine Learning Research
Interactive Data Visualization: Foundations, Techniques, and Applications
Interactive Data Visualization: Foundations, Techniques, and Applications
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Generative topographic mapping (GTM) provides a flexible statistical model for unsupervised data inspection and topographic mapping. Since it yields to an explicit mapping of a low-dimensional latent space to the observation space and an explicit formula for a constrained Gaussian mixture model induced thereof, it offers diverse functionalities including clustering, dimensionality reduction, topographic mapping, and the like. However, it shares the property of most unsupervised tools that noise in the data cannot be recognized as such and, in consequence, is visualized in the map. The framework of visualization based on auxiliary information and, more specifically, the framework of learning metrics as introduced in [14,21] constitutes an elegant way to shape the metric according to auxiliary information at hand such that only those aspects are displayed in distance-based approaches which are relevant for a given classification task. Here we introduce the concept of relevance learning into GTM such that the metric is shaped according to auxiliary class labels. Relying on the prototype-based nature of GTM, efficient realizations of this paradigm are developed and compared on a couple of benchmarks to state-of-the-art supervised dimensionality reduction techniques.