Deep metric learning for sequential data using approximate information

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Samenvatting

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.

Originele taal-2Engels
TitelMachine Learning and Data Mining in Pattern Recognition - 14th International Conference, MLDM 2018, Proceedings
RedacteurenPetra Perner
Plaats van productieCham
UitgeverijSpringer
Pagina's269-282
Aantal pagina's14
ISBN van elektronische versie978-3-319-96136-1
ISBN van geprinte versie978-3-319-96135-4
DOI's
StatusGepubliceerd - 8 jul 2018
Evenement14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018 - New York, Verenigde Staten van Amerika
Duur: 15 jul 201819 jul 2018

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10934 LNAI
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

Congres

Congres14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018
Land/RegioVerenigde Staten van Amerika
StadNew York
Periode15/07/1819/07/18

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