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English(EN) Robust Low-Tubal-Rank Tensor Completion under Cross-Concentrated Sampling

新的R-ItCUR算法增强了张量填充的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为鲁棒迭代t-CUR（R-ItCUR）的新算法，用于从可能包含显著异常值的局部数据中填充低管秩张量。该方法对采样张量交叉进行分区，应用自适应Welsch校正来抑制异常值，并通过投影分块梯度下降更新低秩分量。R-ItCUR直接在采样交叉上操作，降低了内存和计算需求。在合成数据、心脏MRI和地震数据上的实验表明，该方法能够准确恢复数据并对严重损坏具有鲁棒性，强调了利用交叉集中采样结构的优势。 AI

影响 引入了一种更鲁棒的张量填充方法，有望改善医学成像和地震数据处理等领域的数据分析。

排序理由 关于张量填充新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新的R-ItCUR算法增强了张量填充的鲁棒性

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关于张量填充新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Hanqin Cai, Longxiu Huang, Jing Qin, Chengyue Wu ·

    跨浓度采样下的鲁棒低管秩张量补全

    arXiv:2608.03928v1 Announce Type: new Abstract: 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…

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