Summary of Supervised Multi-modal Fission Learning, by Lingchao Mao et al.
Supervised Multi-Modal Fission Learning
by Lingchao Mao, Qi wang, Yi Su, Fleming Lure, Jing Li
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposed Multi-Modal Fission Learning (MMFL) model simultaneously identifies globally joint, partially joint, and individual components underlying the features of multimodal datasets, leveraging supervision from response variables. Unlike existing latent variable methods, MMFL can incorporate incomplete multimodal data and outperforms various algorithms in both complete and incomplete modality settings. This model was applied to a real-world case study for early prediction of Alzheimer’s Disease using multimodal neuroimaging and genomics data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, providing more accurate predictions and better insights into within- and across-modality correlations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MMFL is a new way to analyze datasets that have different types of information. This helps improve predictions by using all the available data. The model works by finding patterns in the data that are shared between different parts, as well as unique features that only appear in certain sections. This approach can handle incomplete or missing data and produces better results than other methods. |
Keywords
» Artificial intelligence » Multi modal
