The paper introduces Coupled Tensor‑Tensor Completion (CTTC), a new framework that incorporates side information in tensor form to enhance tensor completion tasks. CTTC leverages hidden connections among multimodal tensors and is grounded in distance metric learning and group theory. Experiments on the DTD and LINCS datasets show that CTTC outperforms existing methods such as HaLRTC, CTRC, Cell, and NTDDR in both run‑time and root‑sum‑of‑errors accuracy for predicting drug effects.
arXiv:2608. 13234v1 Announce Type: new Abstract: In order to understand complex systems such as the human metabolome or human brain, different sensing technologies are used, generating complex data.
By Gaute Johannessen, Geert Roelof van der Ploeg, Evrim Acar
arXiv:2606. 08188v1 Announce Type: cross Abstract: Matrix completion has been extensively studied for real-valued data, but existing methods are often limited in handling categorical variables.
By Qian Zhang, Meixia Lin
The paper introduces an unsupervised machine learning framework that fuses heterogeneous genomic and clinical data using tensor decomposition and clustering to predict cancer risk levels. By extracting latent, biologically meaningful features through tensor decomposition, the approach outperforms unimodal feature sets. Experiments on LinkedOmics data of 11,158 patients across 32 cancer types show that the tSVD+GMM configuration consistently achieves superior performance on clinical benchmarks such as tumor purity, pathological stage, and survival outcomes.
By Ali Anaissi, Yuanzhe Jia, Qishun Peng, Weidong Huang, Ali Braytee, Junaid Akram
Tensor Completion using Subspace Information (TCSI) is an algorithm that leverages side information by estimating a subspace and reformulating tensor completion as a matrix regression problem. Theoretical analysis shows that accurate subspace information reduces sample complexity to nearly linear in the uncoupled ambient dimensions and relaxes signal-to-noise ratio requirements compared to existing guarantees. Numerical simulations and an application to reconstructing global Total Electron Content (TEC) maps demonstrate lower reconstruction errors than competing methods.
By Jingyang Li, Michael K. Ng
In order to understand complex systems such as the human metabolome or human brain, different sensing technologies are used, generating complex data. These datasets are often multiway, i.
arXiv:2603.02720v2 Announce Type: replace
Abstract: Recently, tensor decompositions have attracted increasing attention. Fundamentally, different interactions among factors induce distinct tensor dec...
By Ting-Wei Zhou, Xi-Le Zhao, Sheng Liu, Wei-Hao Wu, Yu-Bang Zheng, Deyu Meng
arXiv:2506.04166v3 Announce Type: replace
Abstract: Nearest neighbor (NN) methods have re-emerged as competitive tools for matrix completion, offering strong empirical performance and recent theoreti...
By Caleb Chin, Aashish Khubchandani, Harshvardhan Maskara, Kyuseong Choi, Jacob Feitelberg, Albert Gong, Manit Paul, Tathagata Sadhukhan, Dwaipayan Saha, Anish Agarwal, Raaz Dwivedi
arXiv:2606. 12362v1 Announce Type: cross Abstract: We study multimodal learning under missing modalities, with particular motivation from bioscience applications in which heterogeneous modalities are often only partially available when decisions need to be made.
By Hui Wang, Tianyu Ren, Joseph Butler, Christopher Baker, Karen Rafferty, Simon McDade
arXiv:2607. 17390v1 Announce Type: cross Abstract: Kernel regression with tensor trains and Hadamard overparameterization (KReTTaH) is introduced as a training-data-free, interpretable, and nonparametric framework for multi-way data imputation.
By Duc Thien Nguyen, Konstantinos Slavakis, Eleftherios Kofidis, Dimitris Pados
arXiv:2607. 02928v1 Announce Type: new Abstract: Cold-start drug-drug interaction (DDI) prediction for new drugs is critical for minimizing unexpected adverse drug reactions.
By Di Wu, Hongyi Sun, Haichao Xu, Jia Chen, Zhong Chen, Jie Yang
arXiv:2606. 04176v1 Announce Type: new Abstract: We study a distributional generalization of the matrix completion problem in which each entry of the target matrix is a probability distribution rather than a scalar.
By Jiayi Wang, Raymond K. W. Wong