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

arXiv: https://arxiv.org/abs/2105.03682 · HTML: https://mlsanity.com/paper/2105.03682/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 85 references that are in the map.
- First posted on arXiv 8 May 2021.

## Builds on (most cited first, 12 of 12)

- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [SpectralNet: Spectral Clustering using Deep Neural Networks](https://mlsanity.com/paper/1801.01587/) (2018)
- [A Review of Feature Selection Methods Based on Mutual Information](https://mlsanity.com/paper/1509.07577/) (2015)
- [Deep Residual Correction Network for Partial Domain Adaptation](https://mlsanity.com/paper/2004.04914/) (2020)
- [Joint Geometrical and Statistical Alignment for Visual Domain Adaptation](https://mlsanity.com/paper/1705.05498/) (2017)
- [Transfer Learning with Dynamic Distribution Adaptation](https://mlsanity.com/paper/1909.08531/) (2019)
- [Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis](https://mlsanity.com/paper/2001.02015/) (2020)
- [A spectral method for community detection in moderately-sparse degree-corrected stochastic block models](https://mlsanity.com/paper/1506.08621/) (2015)
- [Optimal Laplacian regularization for sparse spectral community detection](https://mlsanity.com/paper/1912.01419/) (2019)
- [Beyond Pham's algorithm for joint diagonalization](https://mlsanity.com/paper/1811.11433/) (2018)
- [EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines](https://mlsanity.com/paper/1403.0745/) (2014)
- [Disaggregation of Remotely Sensed Soil Moisture in Heterogeneous Landscapes using Holistic Structure based Models](https://mlsanity.com/paper/1501.07680/) (2015)

## Built on by (most cited first, 2 of 2)

- [A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection](https://mlsanity.com/paper/2112.04388/) (2021)
- [MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy](https://mlsanity.com/paper/2210.10842/) (2022)

## Abstract (arXiv)

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.