An empirical bayes approach to partially labeled and shuffled data sets

Alex Dytso, H. Vincent Poor

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

Abstract

This work outlines a method for an application of empirical Bayes in the setting of semi-supervised learning. That is, we consider a scenario in which the training set is partially or entirely unlabeled. In addition to the missing labels, we also consider a scenario where the available training data might be shuffled (i.e., the features and labels are not matched). Specifically, we propose to train model-based empirical Bayes separately on the set of features and the set of labels and combine/mix the two models based on the proportion of unlabeled pairs. The method then can be used to recover the missing labels (i.e., create pseudo-labels) of the data set and, in addition, if the data is shuffled, recover the correct permutation of the data. The technique is evaluated for a multivariate Gaussian model and is shown to consistently outperform a maximum likelihood approach. Moreover, the procedure is shown to be a consistent estimator for a multivariate Gaussian model with an arbitrary (non-degenerate) covariance matrix.

Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8886-8890
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: May 4 2020May 8 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
CountrySpain
CityBarcelona
Period5/4/205/8/20

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Dytso, A., & Poor, H. V. (2020). An empirical bayes approach to partially labeled and shuffled data sets. In 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings (pp. 8886-8890). [9054638] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP40776.2020.9054638