Separable Decomposition for Ragged Tensors

Summary

Classical tensor decomposition methods cannot handle ragged tensors, which have irregular index patterns. The authors propose a CP-based, geometry-aware separable decomposition framework that models the valid domain with a binary weighting tensor and decouples the objective into independent subproblems. The method uses a domain-adapted proximal alternating minimization solver with a convergence guarantee, and it reports superior accuracy and efficiency on multispectral, hyperspectral and spatial transcriptomics data.