Semi-supervised clustering using similarity neural networks

Authors:
Stefano Melacci;Marco Maggini;Lorenzo Sarti
Affiliations:
Department of Information Engineering, University of Siena, Siena, Italy;Department of Information Engineering, University of Siena, Siena, Italy;Department of Information Engineering, University of Siena, Siena, Italy
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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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 VCI repository show that SNN based clustering almost always outperforms other methods proposed in the literature.