Boosting Classification Based Similarity Learning by using Standard Distances

Metric learning has been shown to outperform standard classification based similarity learning in a number of different contexts. In this paper, we show that the performance of classification similarity learning strongly depends on the sample format used to learn the model. We also propose an enriched classification based set-up that uses a set of standard distances to supplement the information provided by the feature vectors of the training samples. The method is compared to state-of-the-art metric learning methods, using a linear SVM for classification. Results obtained show comparable performances, slightly in favour of the method