@inproceedings{1a812ec0bf7f429f9ccd5650a92bff64,
title = "Deep metric learning for sequential data using approximate information",
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.",
keywords = "Deep learning, Efficient metric learning, Triplet network",
author = "Stefan Thaler and Vlado Menkovski and Milan Petkovic",
year = "2018",
month = jul,
day = "8",
doi = "10.1007/978-3-319-96136-1_22",
language = "English",
isbn = "978-3-319-96135-4",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "269--282",
editor = "Petra Perner",
booktitle = "Machine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings",
address = "Germany",
note = "14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018 ; Conference date: 15-07-2018 Through 19-07-2018",
}