AI RESEARCH
Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis
arXiv CS.LG
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ArXi:2605.13312v1 Announce Type: new We present Supervised Deep Multimodal Matrix Factorization (SD3MF), an interpretable framework for integrative brain network analysis that generalizes Symmetric Nonnegative Matrix Tri-Factorization (SNMTF) from unsupervised single-graph clustering to supervised prediction over populations of multimodal graphs. SD3MF learns deep hierarchical factorizations for each modality together with a shared latent representation that aligns subjects across views.