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Tensor Ensemble Learning

Citation Author(s):
Ahmad Moniri
Submitted by:
ILYA KISIL
Last updated:
23 November 2018 - 1:09pm
Document Type:
Presentation Slides
Document Year:
2018
Event:
Presenters Name:
Ilia Kisil, Ahmad Moniri, Danilo P. Mandic
Paper Code:
GLOBALSIP18001

Abstract 

Abstract: 

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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IK_AM_DPM_GlobalSIP_2018_presentation.pdf

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