Semi-supervised hierarchy learning using multiple-labeled data

Ailar Javadi, Alexander Gray, David Anderson, Visar Berisha

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

Abstract

While hierarchical semi-supervised classification methods have been previously studied, we still lack an algorithm that can learn a non-predefined categorical hierarchy from multi-labeled data at various levels of specificity. Inspired by human psychology and learning experience, in this paper we propose a semi-supervised learning method that can classify multi-labeled data into a hierarchy based on the label's specificity level such that the separability between each class and its siblings is greater than the separability between each class and its parents. To build the hierarchy we show that a minimum spanning tree minimizes an upper bound on the pairwise Kullback-Liebler divergence between the true and approximated distributions. We show the effectiveness of our method using three types of data sets and draw a comparison between our learned hierarchy and one learned by human subjects using the same data set. We also show the effectiveness of our method compared to hierarchical clustering.

Original languageEnglish (US)
Title of host publication2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011
DOIs
StatePublished - Dec 5 2011
Event21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 - Beijing, China
Duration: Sep 18 2011Sep 21 2011

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing

Other

Other21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
CountryChina
CityBeijing
Period9/18/119/21/11

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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  • Cite this

    Javadi, A., Gray, A., Anderson, D., & Berisha, V. (2011). Semi-supervised hierarchy learning using multiple-labeled data. In 2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011 [6064565] (IEEE International Workshop on Machine Learning for Signal Processing). https://doi.org/10.1109/MLSP.2011.6064565