Short summary: We extend the state of the art of using neural networks to learn similarity measures (learning distance between pairs of data points in a dataset) with a new method called Extended Siamese Neural Networks (eSNN). This new method is put into the context of a framework that is used to describe different types of similarity measures. eSNN outperforms all current methods, while at the same time requiring the least training of all methods (down to half of the training of some comparable methods).
Paper link (Open Access): https://link.springer.com/article/10.1007/s13748-019-00201-2
PDF (Open Access): https://link.springer.com/content/pdf/10.1007%2Fs13748-019-00201-2.pdf
Code: https://github.com/ntnu-ai-lab/eSNN/

Authors: Bjørn Magnus Mathisen, Agnar Aamodt, Kerstin Bach and Helge Langseth

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 a 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, datasets 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 toward 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 toward this goal which relies on either very long training times or manually modeling parts of the similarity measure. We created a framework to help us analyze the current methods for learning similarity measures. This analysis resulted in two novel similarity measure designs: The first design uses a pre-trained classifier as basis for a similarity measure, and the second design uses as little modeling as possible while learning the similarity measure from data and keeping training time low. 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.

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