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.
Bibliographical noteFunding Information:
Manuscript received December 5, 2017; revised May 10, 2018; accepted July 14, 2018. Date of publication July 27, 2018; date of current version August 10, 2018. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Qingjiang Shi. This work was supported in part by National Science Foundation under Grants 1500713, 1514056, and 1711471, and in part by Ministerio de Economia y Competitividad of the Spanish Government and ERDF funds (TEC2016-75067-C4-2-R,TEC2015-515 69648-REDC,TEC2013-41315-R) and Catalan Government funds (2017 SGR 578 AGAUR). (Corresponding author: Georgios B. Giannakis.) P. A. Traganitis and G. B. Giannakis are with the Department of Electrical and Computer Engineering and the Digital Technology Center, University of Minnesota, Minneapolis MN 55455 USA (e-mail:, email@example.com; geor firstname.lastname@example.org).
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- Ensemble learning
- multiclass classification