  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.
