Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Learning with Recurrent Neural Networks
Learning with Recurrent Neural Networks
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
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Patch clustering for massive data sets
Neurocomputing
Adaptive relevance matrices in learning vector quantization
Neural Computation
Topographic mapping of large dissimilarity data sets
Neural Computation
Prototype-based classification of dissimilarity data
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
White box classification of dissimilarity data
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
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Recently, an extension of popular learning vector quantization (LVQ) to general dissimilarity data has been proposed, relational generalized LVQ (RGLVQ) [10,9]. An intuitive prototype based classification scheme results which can divide data characterized by pairwise dissimilarities into priorly given categories. However, the technique relies on the full dissimilarity matrix and, thus, has squared time complexity and linear space complexity. In this contribution, we propose an intuitive linear time and constant space approximation of RGLVQ by means of patch processing. An efficient heuristic which maintains the good classification accuracy and interpretability of RGLVQ results, as demonstrated in three examples from the biomdical domain.