Blind Multiclass Ensemble Classification

Panagiotis Traganitis, Alba Pages-Zamora, Georgios B. Giannakis

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The rising interest in pattern recognition and data analytics has spurred the development of innovative machine learning algorithms and tools. However, as each algorithm has its strengths and limitations, one is motivated to judiciously fuse multiple algorithms in order to find the 'best' performing one, for a given dataset. Ensemble learning aims at such high-performance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a blind scheme for learning from ensembles of classifiers, using a moment matching method that leverages joint tensor and matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. A rigorous performance analysis is derived and the proposed scheme is evaluated on synthetic and real datasets.

Original languageEnglish (US)
Article number8421667
Pages (from-to)4737-4752
Number of pages16
JournalIEEE Transactions on Signal Processing
Issue number18
StatePublished - Sep 15 2018

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  • Ensemble learning
  • crowdsourcing
  • multiclass classification
  • unsupervised


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