Blind Multiclass Ensemble Classification

Autor:	Traganitis, Panagiotis A., Pagès-Zamora, Alba, Giannakis, Georgios B.
Rok vydání:	2017
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
DOI:	10.1109/TSP.2018.2860562
Popis:	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. Comment: To appear in IEEE Transactions in Signal Processing
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1712.02903 Zobrazit plný text záznamu View this record from Arxiv