A Deep Learning Approach to Unsupervised Ensemble Learning

Uri Shaham, Xiuyuan Cheng, Omer Dror, Ariel Jaffe, Boaz Nadler, Joseph Chang, Yuval Kluger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning. First, we prove that the popular model of Dawid and Skene, which assumes that all classifiers are conditionally independent, is RBM) with a single hidden node. Hence, under this model, the posterior probabilities of the true labels can be instead estimated via a trained RBM. Next, to address the more general case, where classifiers may strongly violate the conditional independence assumption, we propose to apply RBM-based Deep Neural Net (DNN). Experimental results on various simulated and real-world datasets demonstrate that our proposed DNN approach outperforms other state-of-the-art methods, in particular when the data violates the conditional independence assumption.
Original languageEnglish
Title of host publicationProceedings of The 33rd International Conference on Machine Learning
EditorsMaria Florina Balcan, Kilian Q. Weinberger
Place of PublicationNew York, New York, USA
PublisherPMLR
Pages30-39
Number of pages10
StatePublished - 1 Aug 2016
Event33rd International Conference on Machine Learning, ICML 2016 - New York City, United States
Duration: 19 Jun 201624 Jun 2016

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume48
ISSN (Electronic)2640-3498

Conference

Conference33rd International Conference on Machine Learning, ICML 2016
Country/TerritoryUnited States
CityNew York City
Period19/06/1624/06/16

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