Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data
arXiv:2606.05488v1 Announce Type: cross Abstract: 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 bicluste
This paper addresses critical analytical challenges in high-dimensional, sparse, and irregularly observed longitudinal omics data, reflecting a growing need for more sophisticated AI-driven tools in biomedical research.
The proposed Tri-SfSVD framework could significantly improve the identification of disease subtypes and personalized treatment strategies, particularly in complex conditions like Inflammatory Bowel Disease.
This research provides a more robust and unified method for biclustering and triclustering longitudinal data, moving beyond the limitations of conventional analytical approaches.
- · Biomedical researchers
- · Precision medicine initiatives
- · AI/ML healthcare companies
- · Pharmaceutical R&D
- · Traditional statistical methods
- · Clinical trial timelines (potentially shortened)
Improved understanding and stratification of complex diseases like IBD.
Accelerated development of targeted therapies and diagnostics for stratified patient groups.
Potential for a new wave of data-driven personalized medicine applications across various chronic conditions.
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Read at arXiv cs.LG