Self-Organizing Maps
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
Soft learning vector quantization
Neural Computation
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Kernel Neural Gas Algorithms with Application to Cluster Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
A Novel Kernel Prototype-Based Learning Algorithm
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Universal Approximation Capability of Cascade Correlation for Structures
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
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)
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Margin-based active learning for LVQ networks
Neurocomputing
Dynamics and Generalization Ability of LVQ Algorithms
The Journal of Machine Learning Research
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
Distance learning in discriminative vector quantization
Neural Computation
Computational capabilities of graph neural networks
IEEE Transactions on Neural Networks
Adaptive relevance matrices in learning vector quantization
Neural Computation
Topographic mapping of large dissimilarity data sets
Neural Computation
Consistency of functional learning methods based on derivatives
Pattern Recognition Letters
Prototype-based classification of dissimilarity data
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
IEEE Transactions on Information Theory
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
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Prototype-based methods often display very intuitive classification and learning rules. However, popular prototype based classifiers such as learning vector quantization (LVQ) are restricted to vectorial data only. In this contribution, we discuss techniques how to extend LVQ algorithms to more general data characterized by pairwise similarities or dissimilarities only. We propose a general framework how the methods can be combined based on the background of a pseudo-Euclidean embedding of the data. This covers the existing approaches kernel generalized relevance LVQ and relational generalized relevance LVQ, and it opens the way towards two novel approach, kernel robust soft LVQ and relational robust soft LVQ. Interestingly, also unsupervised prototype based techniques which are based on a cost function can be put into this framework including kernel and relational neural gas and kernel and relational self-organizing maps (based on Heskes' cost function). We demonstrate the performance of the LVQ techniques for similarity or dissimilarity data in several benchmarks, reaching state of the art results.