Learning Distance Metrics for Multi-Label Classification

Henry Gouk, Bernhard Pfahringer, Michael Cree

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

Abstract

Distance metric learning is a well studied problem in the field of machine learning, where it is typically used to improve the accuracy of instance based learning techniques. In this paper we propose a distance metric learning algorithm that is specialised for multi-label classification tasks, rather than the multiclass setting considered by most work in this area. The method trains an embedder that can transform instances into a feature space where Euclidean distance provides an estimate of the Jaccard distance between the corresponding label vectors. In addition to a linear Mahalanobis style metric, we also present a nonlinear extension that provides a substantial boost in performance. We show that this technique significantly improves upon current approaches for instance based multi-label classification, and also enables interesting data visualisations.
Original languageEnglish
Title of host publicationProceedings of The 8th Asian Conference on Machine Learning
EditorsRobert J. Durrant, Kee-Eung Kim
Place of PublicationThe University of Waikato, Hamilton, New Zealand
PublisherPMLR
Pages318-333
Number of pages16
Volume63
Publication statusE-pub ahead of print - 18 Nov 2016
Event8th Asian Conference on Machine Learning - Hamilton, New Zealand
Duration: 16 Nov 201618 Nov 2016
http://www.acml-conf.org/2016/

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume63
ISSN (Electronic)2640-3498

Conference

Conference8th Asian Conference on Machine Learning
Abbreviated titleACML 2016
Country/TerritoryNew Zealand
CityHamilton
Period16/11/1618/11/16
Internet address

Keywords

  • Distance Metric Learning
  • Multi-Label Classification
  • Instance Based Learning

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