Abstract
The rising interest in pattern recognition and data analytics has spurred the development of a plethora of machine learning algorithms and tools. However, as each algorithm has its strengths and weaknesses, one is motivated to judiciously fuse multiple algorithms in order to find the 'best' performing one, for a given dataset. Ensemble learning aims to create a highperformance meta-algorithm, by combining the outputs from multiple algorithms. The present work introduces a simple blind scheme for learning from ensembles of classifiers, using joint matrix factorization. Blind refers to the combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. Performance is evaluated on synthetic and real datasets.
Original language | English (US) |
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Title of host publication | 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 106-110 |
Number of pages | 5 |
ISBN (Electronic) | 9781509059904 |
DOIs | |
State | Published - Mar 7 2018 |
Event | 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada Duration: Nov 14 2017 → Nov 16 2017 |
Publication series
Name | 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings |
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Volume | 2018-January |
Other
Other | 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 |
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Country/Territory | Canada |
City | Montreal |
Period | 11/14/17 → 11/16/17 |
Bibliographical note
Funding Information:Work supported by NSF grants 1500713 and 1514056, as well as by "Ministerio de Economía y Competitividad" of the Spanish Government, ERDF funds (TEC2013-41315-R,TEC2015-69648-REDC,TEC2016-75067-C4-2-R).
Funding Information:
Work supported by NSF grants 1500713 and 1514056, as well as by “Min-isterio de Economía y Competitividad” of the Spanish Government, ERDF funds (TEC2013-41315-R,TEC2015-69648-REDC,TEC2016-75067-C4-2-R) Emails: {traga003@umn.edu, alba.pages@upc.edu, georgios@umn.edu} 1The terms annotator, learner, and classifier will be used interchangeably.
Publisher Copyright:
© 2017 IEEE.
Keywords
- Ensemble learning
- multi-class classification
- unsupervised