Similarity neural networks (SNNs) are a novel neural network model designed to learn similarity measures for pairs of patterns, exploiting binary supervision. SNNs guarantee to compute non negative and symmetric measures, and show good generalization capabilities even if a small set of supervised pairs is used for training. The application of the new model to K-Means like semi-supervised clustering is investigated, introducing a technique that allows the algorithm to compute cluster centroids by means of Backpropagation on the input layer of the SNN, biased by a regularization function. The experiments carried out on some datasets from the UCI repository show that SNN based clustering almost always outperforms other methods proposed in the literature.
Melacci, S., Maggini, M., Sarti, L. (2009). Semi-supervised Clustering with Similarity Neural Networks. In Proceedings of the International Joint Conference on Neural Networks (IJCNN09) (pp.2065-2072). IEEE [10.1109/IJCNN.2009.5178667].
Semi-supervised Clustering with Similarity Neural Networks
MELACCI, STEFANO;MAGGINI, MARCO;SARTI, LORENZO
2009-01-01
Abstract
Similarity neural networks (SNNs) are a novel neural network model designed to learn similarity measures for pairs of patterns, exploiting binary supervision. SNNs guarantee to compute non negative and symmetric measures, and show good generalization capabilities even if a small set of supervised pairs is used for training. The application of the new model to K-Means like semi-supervised clustering is investigated, introducing a technique that allows the algorithm to compute cluster centroids by means of Backpropagation on the input layer of the SNN, biased by a regularization function. The experiments carried out on some datasets from the UCI repository show that SNN based clustering almost always outperforms other methods proposed in the literature.File | Dimensione | Formato | |
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https://hdl.handle.net/11365/36962
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