Abstract
Learning a distance metric provides solutions to many problems where the data exists in a high dimensional space and hand-crafted distance metrics fail to capture its semantical structure. Methods based on deep neural networks such as Siamese or Triplet networks have been developed for learning such metrics. In this paper we present a metric learning method for sequence data based on a RNN-based triplet network. We posit that this model can be trained efficiently with regards to labels by using Jaccard distance as a proxy distance metric. We empirically demonstrate the performance and efficiency of the approach on three different computer log-line datasets.
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Acknowledgment
The work presented in this paper is part of a project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 780495.
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Thaler, S., Menkovski, V., Petkovic, M. (2018). Deep Metric Learning for Sequential Data Using Approximate Information. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_22
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