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Self-Organizing Maps
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Neural Computation
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ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Neurocomputing
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The Journal of Machine Learning Research
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ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
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Visualization and clustering of multivariate data are usually based on mutual distances of samples, measured by heuristic means such as the Euclidean distance of vectors of extracted features. Our recently developed methods remove this arbitrariness by learning to measure important differences. The effect is equivalent to changing the metric of the data space. It is assumed that variation of the data is important only to the extent it causes variation in auxiliary data which is available paired to the primary data. The learning of the metric is supervised by the auxiliary data, whereas the data analysis in the new metric is unsupervised. We review two approaches: a clustering algorithm and another that is based on an explicitly generated metric. Applications have so far been in exploratory analysis of texts, gene function, and bankruptcy. Relationships of the two approaches are derived, which leads to new promising approaches to the clustering problem.