Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Self-Organizing Maps
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
On the Generalization Ability of GRLVQ Networks
Neural Processing Letters
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
Trading convexity for scalability
ICML '06 Proceedings of the 23rd international conference on Machine learning
Fuzzy classification by fuzzy labeled neural gas
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Parametric Embedding for Class Visualization
Neural Computation
Supervised model-based visualization of high-dimensional data
Intelligent Data Analysis
Supervised locally linear embedding with probability-based distance for classification
Computers & Mathematics with Applications
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
Adaptive relevance matrices in learning vector quantization
Neural Computation
Improved locally linear embedding through new distance computing
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Computer Science - Research and Development
Relevance learning in generative topographic mapping
Neurocomputing
Relevance learning in unsupervised vector quantization based on divergences
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
A general framework for dimensionality reduction for large data sets
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
A general framework for dimensionality-reducing data visualization mapping
Neural Computation
Texture feature ranking with relevance learning to classify interstitial lung disease patterns
Artificial Intelligence in Medicine
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Due to the tremendous increase of electronic information with respect to the size of data sets as well as their dimension, dimension reduction and visualization of high-dimensional data has become one of the key problems of data mining. Since embedding in lower dimensions necessarily includes a loss of information, methods to explicitly control the information kept by a specific dimension reduction technique are highly desirable. The incorporation of supervised class information constitutes an important specific case. The aim is to preserve and potentially enhance the discrimination of classes in lower dimensions. In this contribution we use an extension of prototype-based local distance learning, which results in a nonlinear discriminative dissimilarity measure for a given labeled data manifold. The learned local distance measure can be used as basis for other unsupervised dimension reduction techniques, which take into account neighborhood information. We show the combination of different dimension reduction techniques with a discriminative similarity measure learned by an extension of learning vector quantization (LVQ) and their behavior with different parameter settings. The methods are introduced and discussed in terms of artificial and real world data sets.