Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

TL;DR

This paper introduces an adaptive, graph theory-based dimensionality reduction method to improve ensemble and transfer learning in multimodal data analysis, addressing issues of data reliability and distribution heterogeneity.

Contribution

The work presents a novel adaptive dimensionality reduction technique that enhances ensemble and transfer learning performance on multimodal datasets with varying data quality.

Findings

Outperforms state-of-the-art techniques in diverse multimodal datasets

Improves robustness and reliability of learning models

Effective in handling heterogeneous data quality

Abstract

Modern data analytics take advantage of ensemble learning and transfer learning approaches to tackle some of the most relevant issues in data analysis, such as lack of labeled data to use to train the analysis models, sparsity of the information, and unbalanced distributions of the records. Nonetheless, when applied to multimodal datasets (i.e., datasets acquired by means of multiple sensing techniques or strategies), the state-of-theart methods for ensemble learning and transfer learning might show some limitations. In fact, in multimodal data analysis, not all observations would show the same level of reliability or information quality, nor an homogeneous distribution of errors and uncertainties. This condition might undermine the classic assumptions ensemble learning and transfer learning methods rely on. In this work, we propose an adaptive approach for dimensionality reduction to overcome this issue. By means of a graph theory-based approach, the most relevant features across variable size subsets of the considered datasets are identified. This information is then used to set-up ensemble learning and transfer learning architectures. We test our approach on multimodal datasets acquired in diverse research fields (remote sensing, brain-computer interfaces, photovoltaic energy). Experimental results show the validity and the robustness of our approach, able to outperform state-of-the-art techniques.