Robust Low-Tubal-Rank Tensor Completion under Cross-Concentrated Sampling
Tensor cross-concentrated sampling (t-CCS) bridges entrywise sampling and t-CUR slice-wise sampling by observing entries only within selected horizontal and lateral slices. Existing t-CCS completion methods, however, assume that the observations are free of gross corruption. In this work, we study robust recovery of a third-order low-tubal-rank tensor from partial t-CCS observations contaminated by sparse, arbitrarily large outliers. We propose Robust Iterative t-CUR (R-ItCUR), a tensor-native algorithm that partitions the sampled tensor cross into two exterior blocks and an intersection block, applies adaptive blockwise Welsch correction for outlier suppression, and updates the low-rank ...
| Authors | HanQin Cai, Longxiu Huang, Jing Qin et al. |
|---|---|
| Primary category | stat.ML |
| Published | 4 August 2026 |
| Last updated at source | 5 August 2026 |
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