Sep 2, 2026 · math.NAJ/K move · Enter open · S save
Maryam Bagherian, Albert Hung, Ivo Dinov, Joshua Welch
Department of Mathematics & Statistics, Idaho State University, Physical Science Complex — 921 S. 8th Ave., Stop 8085 — Pocatello, ID 83209 · Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology · Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor · Statistics Online Computational Resource (SOCR), University of Michigan, Ann Arbor · Department of Computer Science & Engineering, University of Michigan, Ann Arbor
Many biomedical challenges can be posed as tensor completion problems where the observed entries of a multidimensional array (a tensor) are used to impute the missing values. In such settings, incorporating side information about the modes of the tensor, such as gene-gene similarity, can significantly enhance the solutions of the completion problem. Most existing tensor completion methods can only incorporate side information in the form of matrices. In this study, we introduce a novel framework to incorporate side information in the form of tensors. Our new approach, called Coupled Tensor-Tensor Completion (CTTC), leverages the hidden connections among multimodal tensors to improve tensor completion performance. In addition to practical utility, CTTC has theoretical foundations in distance metric learning and group theory. We derive an alternating algorithm to solve the CTTC optimization problem and establish its convergence to a stationary point. Finally, we show that CTTC outperforms state-of-the-art tensor completion methods at predicting drug effects. Results: Compared with other tensor completion methods, including HaLRTC, CTRC, Cell, and NTDDR, CTTC demonstrates superior run-time and RSE tensor completion accuracy on two benchmark datasets, DTD and LINCS.