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Tensor Ensemble Learning


In big data applications, classical ensemble learning is typically infeasible on the raw input data and dimensionality reduction techniques are necessary. To this end, novel framework that generalises classic flat-view ensemble learning to multidimensional tensor- valued data is introduced. This is achieved by virtue of tensor decompositions, whereby the proposed method, referred to as tensor ensemble learning (TEL), decomposes every input data sample into multiple factors which allows for a flexibility in the choice of multiple learning algorithms in order to improve test performance. The TEL framework is shown to naturally compress multidimensional data in order to take advantage of the inherent multi-way data structure and exploit the benefit of ensemble learning. The proposed framework is verified through the application of Higher Order Singular Value Decomposition (HOSVD) to the ETH-80 dataset and is shown to outperform the classical ensemble learning approach of bootstrap aggregating.

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Paper Details

Ahmad Moniri
Submitted On:
23 November 2018 - 1:09pm
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Presentation Slides
Presenter's Name:
Ilia Kisil, Ahmad Moniri, Danilo P. Mandic
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[1] Ahmad Moniri, "Tensor Ensemble Learning", IEEE SigPort, 2018. [Online]. Available: Accessed: Sep. 20, 2020.
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author = {Ahmad Moniri },
publisher = {IEEE SigPort},
title = {Tensor Ensemble Learning},
year = {2018} }
T1 - Tensor Ensemble Learning
AU - Ahmad Moniri
PY - 2018
PB - IEEE SigPort
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Ahmad Moniri. (2018). Tensor Ensemble Learning. IEEE SigPort.
Ahmad Moniri, 2018. Tensor Ensemble Learning. Available at:
Ahmad Moniri. (2018). "Tensor Ensemble Learning." Web.
1. Ahmad Moniri. Tensor Ensemble Learning [Internet]. IEEE SigPort; 2018. Available from :