Learning similarity measures from data

Progress in Artificial Intelligence manuscript No. (will be inserted by the editor) Abstract Defining similarity measures is a requirement for some machine learning methods. One such method is case-based reasoning (CBR) where the similarity measure is used to retrieve the stored case or set of cases most similar to the query case. Describing a similarity measure analytically is challenging, even for domain experts working with CBR experts. However, data sets are typically gathered as part of constructing a CBR or machine learning system. These datasets are assumed to contain the features that correctly identify the solution from the problem features, thus they may also contain the knowledge to construct or learn such a similarity measure. The main motivation for this work is to automate the construction of similarity measures using machine learning. Additionally, we would like to do this while keeping training time as low as possible. Working towards this, our objective is to investigate how to apply machine learning to effectively learn a similarity measure. Such a learned similarity measure could be used for CBR systems, but also for clustering data in semi-supervised learning, or one-shot learning tasks. Recent work has advanced towards this goal, relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs. Both similarity measures were evaluated on 14 different datasets. The evaluation shows that using a classifier as basis for a similarity measure gives state of the art performance. Finally the evaluation shows that our fully data-driven similarity measure design outperforms state of the art methods while keeping training time low. Keywords Similarity Measure, Data Science, Neural Networks, Data Analytics, Case-Based Reasoning, Similarity Function, Siamese Networks, Similarity metrics, Distance Metrics This work was supported by the Research Council of Norway through the EXPOSED project(grant number 302002390) and the Norwegian Open AI Lab 1 Introduction Many artificial intelligence and machine learning (ML) methods, such as k-nearest neighbors (k-NN) rely on a similarity (or distance) measure [21] between data points. In Case-based reasoning (CBR) a simple k-NN or a more complex similarity function is used to retrieve the stored cases that are most similar to the current query case.