Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data
Identifying subtypes of complex conditions, such as Inflammatory Bowel Disease (IBD), often requires capturing latent patterns in longitudinal omics data. However, these data are typically high-dimensional, sparsely sampled, and irregularly observed over time, posing substantial challenges for conventional (bi)clustering and functional data analysis methods. We propose Tri-SfSVD, a unified sparse functional Singular Value Decomposition framework for discovering biclusters and triclusters in longitudinal data. Unlike existing functional biclustering methods that rely on ad hoc imputation or enforce restrictive shape-homogeneity assumptions, Tri-SfSVD integrates continuous trajectory estima...
| Authors | Yue Zhao, Thierry Chekouo, Sandra Safo |
|---|---|
| Primary category | stat.ML |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
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