Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis
Read the original on arXiv Machine Learning →The paper introduces Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework that extends Symmetric Nonnegative Matrix Tri-Factorization to supervised prediction across populations of multimodal brain graphs. SD3MF learns deep hierarchical factorizations for each modality and a shared latent representation, jointly optimizing graph reconstruction and prediction while enabling data-driven multimodal fusion. Experiments on multimodal connectome datasets demonstrate that SD3MF outperforms strong deep learning baselines such as CNNs and GNNs, providing biologically interpretable insights through community-level interaction matrices.
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