Robust Low-Tubal-Rank Tensor Completion under Cross-Concentrated Sampling

arXiv:2608.03928 · stat.ML, cs.IT, cs.LG, math.NA · Submitted 2026-08-04 · Read on arXiv

HanQin Cai, Longxiu Huang, Jing Qin, Chengyue Wu

stat.ML, cs.IT, cs.LG, math.NA

Submitted: 2026-08-04

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Cross-Concentrated Sampling — 13× in this paper
• Robustness — 13× in this paper · explained in 6 episodes
• Sparsity — 10× in this paper · explained in 5 episodes
• Matrix Completion — 7× in this paper · explained in 4 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Nuclear Norm — 3× in this paper · explained in 2 episodes
• Objective Function — 2× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational Efficiency — 1× in this paper · explained in 2 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 4 episodes
• False Negatives — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Nadia — 1× in this paper · explained in 2 episodes
• Performance Gap — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
• Collaborative Filtering and the Missing at Random Assumption
• Guaranteed Sampling Flexibility for Low-tubal-rank Tensor Completion
• A large annotated medical image dataset for the development and evaluation of segmentation algorithms

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