Combining Dimensions and Features in Similarity-Based Representations

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Daniel Navarro, Michael Lee


This paper develops a new representational model of similarity data that combines continuous dimensions with discrete features. An al- gorithm capable of learning these representations is described, and a Bayesian model selection approach for choosing the appropriate number of dimensions and features is developed. The approach is demonstrated on a classic data set that considers the similarities between the numbers 0 through 9.