  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.
