2021/05/08 by Andrea Marinoni, Marinoni, Andrea, Saloua Chlaily +12
Computer Science · Engineering · #Data Stream Mining Techniques #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Remote-Sensing Image Classification #cs.LG
paper · pdf · doi:10.48550/arxiv.2105.03682
18 pages, 10 figures, submitted to Pattern Recognition
arxiv created 2021/05/08 · openalex publication_date 2021/05/08 · arxiv updated 2021/05/11 · openalex created_date 2021/05/24 · openalex updated_date 2026/07/28
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.