# Tensor Ensemble Learning for Multidimensional Data

## TL;DR

This paper introduces Tensor Ensemble Learning (TEL), a novel framework that leverages tensor decompositions to enable ensemble learning on multidimensional data, improving performance over traditional methods.

## Contribution

The paper proposes a new TEL framework that generalizes ensemble learning to tensor data using tensor decompositions, enhancing data compression and classification accuracy.

## Key findings

- TEL outperforms classical bootstrap aggregating on ETH-80 dataset.
- Tensor decompositions enable flexible algorithm choices for multidimensional data.
- TEL effectively exploits multi-way data structure for improved learning.

## 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.