Learning from multimodal datasets can leverage complementary information and improve performance in prediction tasks. A commonly used strategy to account for feature correlations in high-dimensional datasets is the latent variable approach. Several latent variable methods have been proposed for multimodal datasets. However, these methods either focus on extracting the shared component across all modalities or on extracting both a shared component and individual components specific to each modality. To address this gap, we propose a Multi-Modal Fission Learning (MMFL) model that simultaneously identifies globally joint, partially joint, and individual components underlying the features of multimodal datasets. Unlike existing latent variable methods, MMFL uses supervision from the response variable to identify predictive latent components and has a natural extension for incorporating incomplete multimodal data. Through simulation studies, we demonstrate that MMFL outperforms various existing multimodal algorithms in both complete and incomplete modality settings. We applied MMFL to a real-world case study for early prediction of Alzheimers Disease using multimodal neuroimaging and genomics data from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. MMFL provided more accurate predictions and better insights into within- and across-modality correlations compared to existing methods.

翻译：从多模态数据集中学习可以利用互补信息并提升预测任务的性能。处理高维数据集中特征相关性的一种常用策略是潜变量方法。目前已针对多模态数据集提出了多种潜变量方法。然而，这些方法要么专注于提取所有模态间的共享成分，要么同时提取共享成分及各模态特有的独立成分。为弥补这一空白，我们提出了一种多模态裂变学习模型，该模型能同时识别多模态数据集特征背后的全局联合成分、部分联合成分及独立成分。与现有潜变量方法不同，MMFL利用响应变量的监督信息来识别预测性潜成分，并能自然扩展至处理不完整多模态数据。通过模拟研究，我们证明MMFL在完整与不完整模态设置下均优于多种现有多模态算法。我们将MMFL应用于阿尔茨海默病早期预测的真实案例研究，使用了来自阿尔茨海默病神经影像倡议数据集的多模态神经影像与基因组学数据。相较于现有方法，MMFL提供了更精确的预测结果，并对模态内与跨模态相关性给出了更深入的解析。