Summary of Unified Bayesian Representation For High-dimensional Multi-modal Biomedical Data For Small-sample Classification, by Albert Belenguer-llorens et al.
Unified Bayesian representation for high-dimensional multi-modal biomedical data for small-sample classification
by Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Jussi Tohka, Vanessa Gómez-Verdejo
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The BALDUR algorithm is a novel Bayesian approach that combines multi-modal data views to extract relevant information for classification tasks while pruning out irrelevant features. It integrates dual kernels to handle small sample sizes and provides explainable solutions due to its linear nature. The model was tested on two neurodegeneration datasets, outperforming state-of-the-art models and identifying biomarker-aligned features consistent with scientific literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BALDUR is a new way of analyzing different types of data together to make better predictions. It’s really good at handling situations where there isn’t much data available and it can explain why it made certain decisions. The algorithm was tested on two sets of data related to neurodegeneration and performed better than other methods, which could help researchers identify new biomarkers for this condition. |
Keywords
» Artificial intelligence » Classification » Multi modal » Pruning
