English(EN) Heteroskedastic Canonical Polyadic Tensor Decomposition

新异方差张量分解方法用于脑电图数据

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员引入了异方差正则多面体张量分解（HCP），这是对标准CP分解的改进。HCP除了低秩均值张量外，还使用非恒定、低秩的精度张量来模拟逐项变异性。已开发出一种交替块坐标上升方法，可以从噪声数据中有效地恢复这两个张量，其计算复杂度与CP-ALS相当。HCP的有效性已通过合成实验和涉及脑电图（EEG）数据的应用得到证明。 AI

影响引入了一种新颖的张量分解统计方法，有望改善神经科学等领域的分析。

排序理由该项目是一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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arXiv stat.ML TIER_1 English(EN) · Kyle Ritscher, Carlos Llosa-Vite · 2026-10-02 04:00

异方差正则多面张量分解

arXiv:2610.00498v1 Announce Type: cross Abstract: When minimizing the squared-error loss, the popular CP decomposition can be interpreted as parameter inference in a Gaussian model with a low-rank mean tensor and constant variance across the tensor entries. We introduce heteroske…

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异方差正则多面张量分解

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