English(EN) Dual-Core Tensor Wheel Decomposition with Structure-Cached Acceleration for Tensor Recovery

新型双核张量轮分解提高数据恢复效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员推出了一种新颖的双核张量轮分解（DCTW）方法，旨在提高张量恢复的效率和可扩展性。DCTW通过将中心核分解为两个低阶核来解决传统张量轮分解的计算瓶颈。此外，采用结构缓存加速策略，通过重用中间张量来最大限度地减少冗余计算和内存使用。实验表明，DCTW在重建质量、计算速度和可扩展性方面与现有的张量网络方法相当或更优，特别是在彩色视频补全和鲁棒去噪等应用中。 AI

影响引入了一种更有效的高维数据处理方法，可能使依赖张量运算进行复杂相关性分析的AI模型受益。

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Wenchao Xie, Qingsong Wang · 2026-11-11 05:00

面向张量恢复的双核张量轮分解与结构缓存加速

arXiv:2611.13938v1 Announce Type: new Abstract: Tensor wheel decomposition captures complex correlations in high-dimensional data through interactions between outer-ring factors and a high-order central core. However, updating these components requires the explicit construction a…

本文如何被排名

报道来源 [1]

面向张量恢复的双核张量轮分解与结构缓存加速

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