Bayesian supervised multilabel learning with coupled embedding and classification

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

2 Scopus citations

Abstract

Coupled training of dimensionality reduction and classifica- Tion is proposed previously to improve the prediction per- formance for single-label problems. Following this line of research, in this paper, we introduce a novel Bayesian su- pervised multilabel learning method that combines linear di- mensionality reduction with linear binary classification. We present a deterministic variational approximation approach to learn the proposed probabilistic model for multilabel clas- sification. We perform experiments on four benchmark mul- Tilabel learning data sets by comparing our method with four baseline linear dimensionality reduction algorithms. Exper- iments show that the proposed approach achieves good per- formance values in terms of hamming loss, macro F1, and micro F1 on held-out test data. The low-dimensional em- beddings obtained by our method are also very useful for exploratory data analysis.

Original languageEnglish (US)
Title of host publicationProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
PublisherSociety for Industrial and Applied Mathematics Publications
Pages367-378
Number of pages12
ISBN (Print)9781611972320
DOIs
StatePublished - 2012
Event12th SIAM International Conference on Data Mining, SDM 2012 - Anaheim, CA, United States
Duration: Apr 26 2012Apr 28 2012

Publication series

NameProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Other

Other12th SIAM International Conference on Data Mining, SDM 2012
CountryUnited States
CityAnaheim, CA
Period4/26/124/28/12

ASJC Scopus subject areas

  • Computer Science Applications

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  • Cite this

    Gönen, M. (2012). Bayesian supervised multilabel learning with coupled embedding and classification. In Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012 (pp. 367-378). (Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611972825.32