Learning distance for sequences by learning a ground metric

Bing Su*, Ying Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Learning distances that operate directly on multidimensional sequences is challenging because such distances are structural by nature and the vectors in sequences arc not independent. Generally, distances for sequences heavily depend on the ground metric between the vectors in sequences. We propose to learn the distance for sequences through learning a ground Mahalanobis metric for the vectors in sequences. The learning samples arc sequences of vectors for which how the ground metric between vectors induces the overall distance is given, and the objective is that the distance induced by the learned ground metric produces large values for sequences from different classes and small values for those from the same class. Wc formulate the metric as a parameter of the distance, bring closer each sequence to an associated virtual sequence w.r.t. the distance to reduce the number of constraints, and develop a general iterative solution for any ground-metric-based sequence distance. Experiments on several sequence datascts demonstrate the effectiveness and efficiency of our method.

Original languageEnglish (US)
Title of host publication36th International Conference on Machine Learning, ICML 2019
PublisherInternational Machine Learning Society (IMLS)
Pages10517-10534
Number of pages18
ISBN (Electronic)9781510886988
StatePublished - 2019
Event36th International Conference on Machine Learning, ICML 2019 - Long Beach, United States
Duration: Jun 9 2019Jun 15 2019

Publication series

Name36th International Conference on Machine Learning, ICML 2019
Volume2019-June

Conference

Conference36th International Conference on Machine Learning, ICML 2019
CountryUnited States
CityLong Beach
Period6/9/196/15/19

ASJC Scopus subject areas

  • Education
  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Su, B., & Wu, Y. (2019). Learning distance for sequences by learning a ground metric. In 36th International Conference on Machine Learning, ICML 2019 (pp. 10517-10534). (36th International Conference on Machine Learning, ICML 2019; Vol. 2019-June). International Machine Learning Society (IMLS).